Loss of Nuclear TDP-43 Impairs Lipid Metabolism in Microglia-Like Cells

Loss of Nuclear TDP-43 Impairs Lipid Metabolism in Microglia-Like Cells | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Loss of Nuclear TDP-43 Impairs Lipid Metabolism in Microglia-Like Cells Khushbu Kabra, Dallin Dressman, Ryan Talcoff, Maedot Yidenk, Olivia M. Rifai, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8036170/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease marked by progressive motor neuron loss, with TDP-43 pathology present in over 90% of cases. While neuroinflammation is a recognized hallmark, the role of microglia in ALS pathogenesis remains incompletely understood. Here, we demonstrate that TDP-43 regulates microglial function via triglyceride metabolism. Using shRNA-mediated TARDBP knockdown in human monocyte-derived microglia-like cells (MDMi), we observed suppressed cholesterol biosynthesis, upregulated fatty acid uptake, lipid droplet accumulation, enhanced phagocytic activity, and increased IL-1β production. Inhibiting diacylglycerol acyltransferase (DGAT) enzymes reduced lipid droplet formation, phagocytosis, and IL-1β, directly linking the triglyceride pathway to microglial activation. Patient-derived MDMi from both sporadic and TARDBP -mutant ALS cases showed overlapping as well as distinct alterations, some of which were reversed by DGAT inhibition. Our findings identify dysregulated triglyceride metabolism as a novel pathway through which TDP-43 mediates microglial dysfunction, highlighting a potential therapeutic target for ALS. TDP-43 Lipid Droplets Triglycerides ALS Neurodegeneration Microglia MDMi Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Highlights • TDP-43 nuclear depletion causes increased LD, driven by triglyceride accumulation. • TDP-43 nuclear depletion causes increased phagocytosis and pro-inflammatory cytokine expression. • Inhibiting triglyceride synthesis using DGAT inhibitors rescues LD and pro-inflammatory phenotype in TDP-43 depleted MDMi • ALS patient-derived MDMi display increased LD and expression, rescued by DGAT inhibitors Introduction Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder primarily affecting motor neurons. Although only about 10% of cases are familial, an increasing number of genes with diverse biological functions have been linked to both familial and sporadic forms of ALS [ 1 , 2 ]. Multiple cellular pathways, including protein degradation, mitochondrial dysfunction, and autophagy, to name a few, have been implicated in disease pathogenesis, yet the precise mechanisms remain poorly understood. Despite this genetic and mechanistic heterogeneity, common features such as metabolic dysfunction, neuroinflammation, and microglial dysregulation consistently emerge across ALS cases [ 3 – 5 ]. Microglia are key innate immune cells in the central nervous system (CNS), known to be dysregulated in various neurodegenerative diseases, including ALS [ 6 – 8 ]. Although the presence of neuroinflammation, including activation of glial cells and infiltration of peripherally-derived innate and adaptive immune cells, is a consistent hallmark of ALS [ 9 ], the specific mechanism by which microglia contribute to disease pathology is unclear [ 10 ]. Microglia sequencing studies in ALS have demonstrated significant, distinct alterations in gene expression compared to both healthy subjects and individuals with other neurodegenerative diseases [ 11 – 13 ]. Transcriptomic studies of post-mortem spinal cord tissues implicate glial activation in ALS [ 14 , 15 ]. In particular, chronic or excessive microglial activation has been proposed as a mechanism driving the transition from neuroprotective to neurotoxic phenotypes, making microglia a potential therapeutic target in ALS [ 16 , 17 ]. An important characteristic of microglia is that they are highly mobile and dynamic, constantly modifying their shape and membrane structure to survey the environment and respond to pathogens and injury [ 18 ]. Lipids have a crucial role in regulating membrane structure and are therefore important in microglia function [ 19 ]. Additionally, lipid microdomains are required for the localization of signaling receptors and regulating immune pathways [ 20 – 22 ]. Microglia are also known to be metabolically altered in many neurodegenerative diseases. Notably, lipid droplets (LDs), which store cholesterol esters, triglycerides, and other lipids within cells [ 23 ], have been shown to accumulate in aging as well as Alzheimer’s disease (AD) [ 24 ]. Interestingly, the ApoE4 genotype, which confers significant risk for AD, has been linked to lipid droplet formation in induced pluripotent stem cell (iPSC)-derived microglia. These ‘lipid-laden’ microglia were enriched in cellular senescence genes and shown to drive neurotoxicity [ 25 ]. It is also known that lipopolysaccharide (LPS) stimulation in microglia causes accumulation of LDs, which activate inflammatory pathways in the brain that can become neurotoxic over time and contribute to neuroinflammation [ 26 ]. The mechanisms underlying microglial LD formation in different contexts remain elusive. Studies have shown metabolic dysregulation, specifically lipid alterations, to be a consistent feature in ALS, with numerous studies demonstrating alterations in lipid pathways in both patient biofluids and disease models [ 27 – 29 ]. However, the mechanisms regulating lipid dysfunction in ALS, specifically in microglia, have not been well-studied [ 5 , 30 ]. TARDBP , which encodes the TAR DNA-binding protein 43 (TDP-43), is a key gene implicated in ALS pathology [ 31 ]. TDP-43 aggregation and nuclear depletion are hallmarks in ~ 95% of ALS cases [ 32 ]. Previous studies in oligodendrocytes, motor neurons, as well as HeLa and HEK293 cell lines have demonstrated the role of TDP-43 in regulating lipid metabolism, including cholesterol biosynthesis and efflux pathways, but the mechanisms by which these disruptions impact ALS remain unclear. A recent study showed that overexpression of TARDBP in HEK293 cells resulted in defects in cholesterol biosynthesis [ 33 ], whereas another study showed that conditional knockout of TARDBP in mouse oligodendrocytes suppressed cholesterol biosynthesis gene expression [ 34 ]. Although these studies implicate a role for TDP-43 in regulating cholesterol biosynthesis via SREBP2, the underlying mechanisms remain to be established, as does the impact on immune cells like microglia, and the contribution to ALS pathology. Here, we investigated the impact of TARDBP knockdown on lipid metabolism and microglial function using human MDMi. Previous work has demonstrated that MDMi generated from ALS patient-derived blood display nuclear loss of TDP-43 and exhibit other ALS phenotypes such as altered cytokine expression and function [ 35 ]. We therefore used a TARDBP shRNA-mediated knockdown model to specifically investigate lipid alterations and determine how loss of TDP-43 impacts microglia function. Additionally, we analyzed patient-derived MDMi from both sporadic ALS (sALS) cases and patients with TARDBP mutations (TDP-ALS) and observed both shared and distinct features when compared to the shRNA knockdown model. Results TARDBP knockdown causes nuclear TDP-43 depletion in MDMi To investigate how TDP-43 depletion alters lipids in a microglia model, we employed shRNA-mediated gene knockdown in MDMi, achieving an average TARDBP knockdown efficiency of approximately 70% (Fig. 1 A). The demographic information of donors used can be found in Supplementary Table 1 . Knockdown efficiency was validated by both immunocytochemistry (Fig. 1 B) and Western blot analysis ( Fig. S1 A, B ). Confocal imaging showed a 40% reduction in mean nuclear TDP-43 intensity (Fig. 1 C) and a significant reduction in the nuclear to cytoplasmic ratio of TDP-43 staining ( Fig. S1 C ), but no significant change in cytoplasmic TDP-43 (Fig. 1 D). We also stained for phospho-TDP-43 (pTDP-43), given that pTDP-43 is known to increase in ALS and is a marker for cytosolic TDP-43 accumulation [ 36 , 37 ]. However, there were no significant alterations in nuclear or cytoplasmic pTDP-43 ( Fig. S2 A-C ), or the nuclear to cytoplasmic ratio of pTDP-43 ( Fig. S2 D ). TARDBP knockdown significantly alters lipid metabolism and immune genes in MDMi. To assess the impact of TARDBP knockdown (TDP-43 KD), we used the Fluidigm Biomark microfluidics system to analyze gene expression with a targeted panel of 110 genes related to lipid metabolism (cholesterol, phospholipid/sphingolipid, triglyceride, and fatty acid metabolism), glycolysis, and immune function, and compared results to scramble controls. STRING analysis[ 38 ] shows the genes in three main clusters as expected - immune function, lipid metabolism, and glycolysis, although many of the genes have related functions (Fig. 2 A). As demonstrated in the volcano plot (Fig. 2 B), several genes were significantly altered in the knockdown. Interestingly, of the 110 genes measured, only 27 were upregulated. 14 of these reached statistical significance with an unadjusted p-value < 0.05, while only 2 were significant with an adjusted p-value < 0.05 (Fig. 2 C). Many of these genes were related to immune function, with pro-inflammatory cytokines CCL4 and IL1β being most highly upregulated ( Fig. 2 C). TREM1 , associated with increased inflammatory phenotypes, was also highly upregulated. Among the lipid cluster, fatty acid-associated genes like FABP4 , ELOVL3 , and BSCL2 had the greatest fold-change increase in the knockdown compared to scramble control. These genes are involved in fatty acid uptake and transport, elongation, and lipid droplet formation [ 39 – 42 ]. Of the 110 analyzed genes, 84 were downregulated, with 59 showing statistically significant changes with an unadjusted p-value < 0.05, while 28 of those were significant with an adjusted p-value < 0.05. Among the top 15 downregulated genes, INSIG1 , which encodes the protein INSIG1 that binds to and regulates SREBP2, exhibited the greatest reduction in expression (Log2FC = -2) (Fig. 2 D). This is consistent with a previous study in mouse oligodendrocytes where conditional knockout of TARDBP resulted in a significant reduction in INSIG1 [ 34 ]. Other genes related to INSIG1 , such as SREBP2 and MBTPS1 , were also downregulated in our study, suggesting suppression of cholesterol biosynthesis pathways, again consistent with previous studies [ 34 , 43 ]. Other significantly downregulated genes included TREM2 and LPL , both highly expressed in microglia, and known to be involved in neurodegenerative diseases like AD through their function in regulating microglia lipid homeostasis as well as immune function [ 44 , 45 ]. TREM2 expression has been found to be increased in spinal cords of SOD1-ALS mice and in reactive microglia from ALS postmortem tissues [ 13 , 46 ]. The Alzheimer's disease-associated genetic variant TREM2 R47H, which modulates ligand binding, has also been implicated in ALS, supporting the importance of correctly functioning lipid metabolism in microglia [ 47 , 48 ]. We confirmed the increase in IL1β expression by qPCR and ELISA and found that it was indeed significantly upregulated at both the gene and protein level ( Fig. S3 A, B ). We also measured soluble TREM2 (a biologically active fragment of TREM2) and found a significant reduction in the TARDBP knockdown ( Fig. S3 C ), suggesting a possible downregulation of anti-inflammatory pathways while pro-inflammatory pathways are upregulated (Fig. 2 D). Interestingly, genes related to glycolysis ( PFKP , G6PD , and GLUT1) were among those most significantly downregulated in the knockdown (Fig. 2 D), although hexokinase 1 ( HK1 ) which phosphorylates glucose to G-6-P was significantly upregulated, suggesting alterations in glucose metabolism that could contribute to bioenergetic dysregulation, which is a known feature of ALS [ 49 , 50 ]. TDP-43 depletion causes increased lipid droplet accumulation in MDMi. LD accumulation has emerged as a significant phenotype in diseased/abnormal microglia and has been associated with AD, tauopathies, and aging [ 24 , 25 , 51 ], although the pathomechanism is not clear. Given the role of LD accumulation as a marker for microglia dysfunction, as well as significant alterations of genes like TREM2, LPL, BSCL2 , and FABP4 in our knockdown, we sought to determine whether TDP-43-depleted MDMi have altered LD accumulation. Using LipidTox green to stain for neutral lipids (usually stored in lipid droplets), we found a significant increase in LD intensity in the TARDBP knockdown cells compared to controls (Fig. 3 A, B), pointing to altered lipid metabolism and bioenergetics. TDP-43 depletion does not alter cholesterol uptake in MDMi. Next, we investigated whether the accumulation of LDs was driven by changes in cholesterol uptake or efflux, given the dramatic alterations we observed in the expression of cholesterol-associated genes upon TARDBP knockdown. Under conditions of low intracellular cholesterol levels, INSIG1 dissociates from the SCAP-SREBP2 complex, allowing SREBP2 to translocate to the endoplasmic reticulum (ER) from the Golgi and activate the transcription of various cholesterol biosynthesis genes, including HMGCR [ 52 ]. Downregulation of these genes, as observed in our TARDBP knockdown, may impair the cell’s capacity to activate cholesterol biosynthesis and could increase external uptake of cholesterol. Using a fluorescently labeled analogue of cholesterol (NBD-cholesterol), which has been previously shown to mimic cholesterol uptake via lipoproteins in cells [ 53 – 55 ], we measured the uptake of cholesterol in MDMi. Interestingly, we found no difference in NBD-cholesterol uptake between controls and TARDBP knockdown cells (Fig. 3 C, D). To verify that this assay does in fact recapitulate how cells physiologically uptake cholesterol from the media, we repeated the experiment in the presence of LPS stimulation, as other studies have shown that LPS treatment induces cholesterol uptake in immune cells [ 56 ]. Indeed, we saw that with LPS stimulation, both scramble control and TARDBP knockdown cells exhibit an increase in NBD-cholesterol uptake; however, this increase was only significant in scramble controls ( Fig. S4 A, B ). When comparing the ratio of NBD-cholesterol uptake in LPS-stimulated to unstimulated conditions, we observed significantly lower uptake in the TARDBP knockdown MDMi compared to the scramble control, indicating a reduced response to LPS ( Fig. S4 C ). These findings suggest that although INSIG1 and SREBP2 expression are decreased and cholesterol synthesis may be depressed, TDP-43 depleted MDMi do not increase cholesterol uptake. Additionally, when stimulated with LPS, TDP-43 depleted MDMi do not have a significant induction of cholesterol uptake as seen in the control. Gene expression of SCARB1 , LDLR , and LRP1 , receptors involved in lipoprotein uptake, was also downregulated in the TARDBP knockdown, which could explain why these cells are unable to upregulate cholesterol uptake. Taken together, these data suggest that the lipid droplet accumulation observed in our TARDBP knockdown is not driven by cholesterol uptake. TARDBP knockdown in MDMi reduces cellular total and free cholesterol levels. Our gene expression analysis also revealed downregulation of ABCA1 and ABCC4 , suggesting possible impairment of cholesterol efflux. Indeed, previous reports have shown that ABCA1 deletion causes an accumulation of cholesterol esters as the cell is unable to efflux excess cholesterol [ 57 – 59 ]. To determine whether the lipid droplet accumulation observed in our TARDBP knockdown MDMi is a result of cholesterol ester accumulation, we measured total cholesterol (TC), free cholesterol (FC), and cholesterol esters (CE) in cells and supernatants of scramble and TARDBP knockdown MDMi. Cells are known to uptake free cholesterol from lipoproteins, which are esterified by the enzyme Acyl-coenzyme A: cholesterol acyltransferase-1 (ACAT1) to cholesterol esters, for storage in LDs. TARDBP knockdown cells exhibited decreased TC and FC, with no significant change in CEs (Fig. 4 A-C). Supernatants showed a similar trend, with TC and FC levels being significantly reduced while CE was unchanged (Fig. 4 D-F). The overall reduction in TC and FC suggests reduced cholesterol synthesis in TARDBP knockdown cells, with a concomitant decrease in efflux. There was no significant difference in the ratio of FC to TC in scramble compared to TARDBP knockdown in cells and supernatants, which supports the idea that cholesterol esterification does not drive LD accumulation in these cells ( Fig. S4 D-E ), as increased esterification would reduce the proportion of FC. This is also supported by the NBD-cholesterol uptake data, which showed no significant changes in uptake between scramble and knockdown. To further confirm that the lipid droplet phenotype observed in TARDBP knockdown cells is not a result of cholesterol ester accumulation, we treated the scramble and knockdown MDMi with an ACAT1 inhibitor, which has previously been shown to reduce free cholesterol and cholesterol esters in cells [ 60 ]. As expected, ACAT1 inhibition led to a significant reduction in lipid droplets in scramble control cells; however, no such reduction was observed in TDP-43–depleted MDMi ( Fig. S4 F ), supporting the above finding that cholesterol esterification does not contribute to LD accumulation in these cells. TDP-43 depleted MDMi exhibit increased glycerol and triglyceride levels. LDs are known to be comprised of both cholesterol esters and triglycerides [ 23 , 61 ]. We therefore measured triglyceride levels in these cells to interrogate whether triglyceride, rather than cholesterol ester accumulation, could be contributing to the LD phenotype observed in TARDBP knockdown cells. Triglyceride (TG) levels are calculated by subtracting the difference between total glycerol and free glycerol (which can be used to synthesize TGs). Interestingly, TGs were significantly increased in TARDBP knockdown cells, as were total glycerol levels, whereas free glycerol was not significantly altered (Fig. 5 A-C). No significant alterations were observed in the supernatants (Fig. 5 D-F). However, the cell-to-supernatant ratio of total glycerol and TGs was significantly increased in the knockdown ( Fig. S5 A-C ), while free glycerol remained unchanged. The increase in TGs and total glycerol, with stable free glycerol, suggests enhanced TG synthesis or possible defects in lipolysis. DGAT1/2 inhibitors reduce lipid droplets in TARDBP knockdown MDMi. Microglia have been shown to contain higher levels of TGs compared to other brain cell types, specifically astrocytes and neurons [ 62 ]. Additionally, a recent study found that triglyceride metabolism may be key in regulating microglia inflammatory pathways [ 63 ]. In this study, inhibitors of DGAT 1 and 2 (diacylglycerol acyltransferases 1 and 2), which control the rate-limiting step of triglyceride biosynthesis from diacylglycerol and fatty acids, were used to reduce triglyceride accumulation in iPSC-derived microglia. Given the possibility of increased TG synthesis in our TARDBP knockdown model, we used a similar approach to determine whether DGAT inhibitors could reverse the observed LD phenotype. First, we measured the effect of the DGAT inhibitors on glycerol and TG levels. Unsurprisingly, we found that DGAT inhibitors reduced total glycerol, free glycerol, and TGs in the TARDBP knockdown condition (Fig. 6 A-C). However, no significant changes were seen in the scramble control, suggesting that TG accumulation, and possibly an increase in the activity of DGAT 1 and 2 enzymes, only occurs when nuclear TDP-43 levels are reduced. Interestingly, total glycerol levels were increased in the supernatants of DGAT inhibitor-treated samples, both in scramble and TARDBP knockdown conditions (Fig. 6 D), suggesting that inhibiting triglyceride synthesis either reduces the incorporation of glycerol from the media, or increases the secretion of excess glycerol. As an additional control, we measured glycerol and TG levels in cells treated with ACAT1 inhibitors. As expected, ACAT1 inhibition did not alter total glycerol or TG levels in the scramble or knockdown cells, and there was no change in total glycerol levels in the supernatants ( Fig. S6 A-D ). We then measured LD accumulation in the DGAT inhibitor-treated cells and found a significant decrease in the fluorescence intensity of LipidTox in the TARDBP knockdown in treated compared with untreated cells; this effect was not seen in the scramble controls (Fig. 6 E). Taken together, these data suggest that increased lipid droplet formation in the TARDBP knockdown is a result of TG accumulation, possibly due to increased synthesis driven by DGAT1 and 2. To further interrogate whether TARBDP knockdown MDMi are in fact accumulating triglycerides via upregulated synthesis, we examined the gene expression of DGAT1 and DGAT2 in the knockdown and found, unexpectedly, that their expression was decreased (Fig. S7 A, B ). However, triglyceride hydrolysis genes ATGL and HSL were increased ( Fig. S7 C, D ). This suggests that although TDP-43-depleted MDMi may not be increasing de novo triglyceride synthesis, triglyceride hydrolysis could be upregulated due to increased storage and accumulation. It is also possible that DGAT1 and DGAT2 expression is reduced as a negative feedback mechanism to counter increased enzyme activity. Additionally, DGAT2 is normally localized in the ER, but it is also found within LD membranes and facilitates their expansion at the ER-LD interface. DGAT1, on the other hand, converts exogenous pre-formed fatty acids into triglycerides [ 64 ]. DGAT1 and 2 could therefore be involved in lipid droplet expansion and regulation of exogenous fatty acid uptake, thus resulting in decreased lipid droplet accumulation when they are inhibited. TDP-43 depleted MDMi have increased fatty acid uptake. We then wanted to examine whether TARDBP knockdown MDMi exhibit alterations in fatty acid uptake, which might explain the increase in TG synthesis or storage. FABP4 , involved in regulating fatty acid uptake and transport, and ELOVL3 , involved in fatty acid elongation, were among the most upregulated metabolism genes in the TARDBP knockdown. Additionally, ACSL4 and ACSL1 (Acyl-CoA Synthetase Long-Chain family), which encode enzymes that activate long-chain fatty acids by converting them into fatty acyl-CoA, were also upregulated. Thus, we hypothesized that TDP-43 depletion upregulates fatty acid uptake, which could contribute to increased TG storage and LD accumulation. Indeed, the uptake of BODIPY-labeled C12 fatty acid was significantly increased in the TARDBP knockdown condition, as shown by quantification of confocal imaging (Fig. 7 A, B). TDP-43 depleted MDMi have altered morphology and function. Finally, in addition to the effects on lipid metabolism, we wanted to understand the effect of TARDBP knockdown on conventional microglia functions. Microglia are known to change their morphology depending on their activation state and function. Surveilling and homeostatic microglia are thought to be ramified, whereas activated or phagocytic microglia have an ameboid morphology [ 65 ]. Here, we found that TDP-43-depleted MDMi had a more rounded morphology and a smaller cell body (Fig. 8 A). This was quantified using the “compactness” metric in CellProfiler, where lower values indicate a more rounded morphology (Fig. 8 B) [ 66 ]. Rounded microglia have been associated with enhanced phagocytosis [ 67 ], consistent with previous reports showing that microglia in ALS exhibit a more activated phenotype [ 4 , 35 , 68 , 69 ]. In agreement with this, we found that TDP-43 depleted cells exhibited an increase in dextran uptake, suggesting greater phagocytic activity (Fig. 8 C, D). Phagocytosis and IL1β levels are reduced in TDP-43 depleted MDMi treated with DGAT1 and DGAT2 inhibitors. To confirm that the inflammatory and phagocytic phenotypes observed in the TARDBP knockdown microglia are driven by elevated TG levels, we measured these outcomes in MDMi treated with DGAT inhibitors. We found no changes in cell morphology (Fig. 8 E). However, dextran uptake was significantly reduced in DGAT inhibitor-treated MDMi (Fig. 8 F), and IL1β expression was significantly reduced in the TARDBP knockdown but not in the scramble (Fig. 8 G), which is consistent with the effect of DGAT inhibitors on LD and TG accumulation. We also found that ACAT1 inhibition did not alter IL1β expression (Fig. 8 H), indicating that the IL1β gene expression increase is downstream of TG accumulation. The fact that morphology was not altered upon DGAT inhibition suggests that it may be a result of alterations in cytoskeletal proteins, which are known to be transcriptionally regulated by TDP-43 [ 70 , 71 ]. To understand how inhibiting TG synthesis affects the pathways that were altered by TADRBP knockdown, we analyzed gene expression changes in DGAT inhibitor-treated samples with Fluidigm microfluidic qPCR. Interestingly, we found that most of the cholesterol biosynthesis genes remain downregulated. However, key fatty acid genes and LD-associated genes like FABP4 , ELOVL3 , and BSCL2 , which were upregulated in the knockdown, are reduced with DGAT inhibition. Many upregulated immune genes are also reduced significantly ( Fig. S8 ). To determine whether DGAT inhibition reduces fatty acid uptake, we measured BODIPY-C12 uptake and found a significant reduction in the knockdown, but not in the scramble ( Fig. S9 A ). Fatty acid uptake can also be reduced by directly inhibiting a fatty acid receptor such as CD36 ( Fig. S9 B ); however, we found that direct inhibition of CD36 (with both 4-hour and 24-hour SSO treatment) did not reduce LD intensity ( Fig. S9 C, D ). In fact, LD intensity was increased in control cells with 4-hour SSO treatment ( Fig. S9 C ), suggesting that there may be compensatory effects of inhibiting CD36-mediated fatty acid uptake, or that TDP-43 mediated increases in fatty acid uptake are regulated through more complex mechanisms. Lipidomic analysis of TARDBP knockdown MDMi demonstrates increases in monounsaturated triglycerides. To better understand the lipid profile in the TDP-43 depleted MDMi, we ran targeted LCMS-based lipidomic analysis and found alterations in many lipid species in the knockdown compared to the scramble control. Significant alterations were found in acylcarnitine (AC), triglyceride (TG), diacylglyceride (DG), and monoacylglyceride (MG) species (Fig. 9 A-D). Notable trends in TG alterations included an increase in various mono and poly-unsaturated species of TGs (18:1 and 20:4), which are usually stored in neutral lipid droplets, while saturated TGs (18:0) were reduced (Fig. 9 E), with TG 54:0/18:0 being significantly reduced in the knockdown (p < 0.05). We also found alterations in Bis(monoacylglycero)phosphate (BMP), unique phospholipids found in the inner membranes of late endosomes and lysosomes that play an important role in lysosomal stability and lipid degradation ( Fig. S10 ). Most species of mono-hexosyl ceramides (glucosylceramide), which are hydrolyzed in the lysosome to provide the cell with glucose and ceramide, were decreased, supporting the alterations in BMP that suggest defects in lysosomal degradation. Long-chain ACs (AC C12:0 and C18:0) were decreased while short-chain ACs (AC C3:0) were increased, which could indicate increased oxidation of fatty acids ( Fig. S10 ). Taken together, the lipidomics data suggests alterations in triglyceride and fatty acid metabolism, as well as possible dysfunction in lysosomal degradation. ALS-patient derived MDMi show increased lipid droplets and IL1β, rescued by DGAT inhibition. We obtained peripheral blood mononuclear cells (PBMCs) from three individuals with mutations in TARDBP , one of whom was diagnosed with ALS and two of whom are pre-symptomatic mutation carriers, to make MDMi from ( Fig. S11 A ). All three patients had missense mutations that have been shown to cause TDP-43 loss of function, mislocalisation, and aggregation [ 72 – 76 ], although TARDBP expression as measured by qPCR was unchanged ( Fig. S11 B ). Compared to age and sex-matched controls, we found a significant increase in both IL1β gene expression and protein levels in the supernatants of the TARDBP -mutant (TDP-ALS) MDMi, which were reduced with DGAT inhibitor treatment (Fig. 10 A, B). We also obtained PBMCs from patients with sporadic ALS (sALS) ( Supplementary Table 2 ) and observed the same increase in IL1β expression and protein levels in their MDMi (Fig. 10 C-D). LD intensity was increased in the TDP-ALS MDMi, but this only reached significance when including every image in the dataset rather than the mean of all images for each sample (Fig. 10 E and Fig. S11 D ) and was rescued with DGAT inhibitor treatment (Fig. 10 E and Fig. S11 C-D ). We also measured mean LD area and found that this was not significantly altered (Fig. 10 F), although the maximum LD radius was significantly higher in the TDP-ALS MDMi compared to controls (Fig. 10 G), as seen in confocal images (Fig. 10 H). DGAT inhibition did not have a significant effect on LD area or maximum radius in TDP-ALS MDMi (Fig. 10 F and Fig. S11 E-F ), and in fact, DGAT inhibitor treatment seems to slightly increase LD area even though mean intensity was reduced. The sALS MDMi, on the other hand, showed a significant decrease in mean LipidTox intensity (Fig. 10 I), whereas LD area and maximum radius were significantly increased (Fig. 10 J, K), as also evident in imaging data (Fig. 10 L). Again, significant trends were seen when every image was included in a nested analysis rather than using the mean of each donor ( Fig. S11 H, I ), pointing to large variability between cells. This suggests that, while overall there were fewer LDs in sALS MDMi, resulting in lower mean intensity, there was a greater accumulation of large LDs. Finally, we performed Fluidigm microfluidic qPCR analysis on the patient-derived MDMi, along with healthy control samples and DGAT inhibitor-treated samples (Fig. 11 ). Notable differences were observed between TDP-ALS and sALS samples compared to healthy controls ( Fig. S12 A ). Principal component analysis (PCA) revealed a clear separation between TDP-ALS MDMi and their age and sex-matched controls ( Fig. S12 B ). In contrast, the separation between sALS samples and their matched controls was less distinct, suggesting greater heterogeneity or subtler transcriptional changes in the sALS group ( Fig. S12 C ). Volcano plots show gene alterations in both TDP-ALS MDMi, and sALS MDMi compared to matched controls ( Fig S12 D-E ). There were not many genes significantly altered, even with unadjusted p-values, presumably due to the low sample numbers and individual heterogeneity. We also found divergent gene expression patterns between the patient-derived MDMi and shRNA knockdown MDMi. The TDP-ALS MDMi showed increased expression of many cholesterol biosynthesis genes ( DHCR7 , INSIG1 , SREBP2 ), which were reduced with DGAT inhibitors (Fig. 11 ). This contrasts with the knockdown, where SREBP2 and INSIG1 were significantly downregulated (Fig. 2 D). Some consistencies with the knockdown were the increased expression of triglyceride and fatty acid metabolism genes in the TDP-ALS MDMi, which are not seen in the sALS MDMi (including FABP4 , FADS1 , MGAT1 , and DGAT2 ) (Fig. 11 ). These data suggest that altered fatty acid and triglyceride metabolism may be specific to mutations in TARDBP , and possibly nuclear depletion of TDP-43, whereas in sALS cases, there may be other factors contributing to disrupted lipid metabolism that cause LD accumulation and altered immune responses. Taken together, our data demonstrate that TDP-43 plays a critical role in regulating lipid metabolism in microglia. Specifically, nuclear depletion of TDP-43 results in an accumulation of TGs, likely via increased uptake of fatty acids and upregulated TG storage caused by bioenergetic alterations. TDP-43 nuclear depletion also results in increased phagocytic capacity of microglia and higher secretion of pro-inflammatory cytokines like IL1β, which is downstream of TG accumulation. Patients with mutations in TARDBP exhibit similar phenotypes, which are reversed with DGAT inhibition, suggesting that lipid pathways, specifically TG metabolism may be dysregulated in TARDBP -driven ALS. MDMi from sALS patients also accumulate large LDs, but show distinct gene expression profiles, suggesting a different mechanism of LD accumulation. Discussion Although mutations in TARDBP are rare and make up only 5% of the total familial ALS population, TDP-43 pathology is present in over 90% of ALS patients. Additionally, it is observed in over 50% of Alzheimer's disease (AD) cases and some Parkinson's disease (PD) cases [ 77 – 81 ]. Rather than focusing on specific TARDBP mutations, we employed a general loss-of-function model to investigate the broader cellular consequences of TDP-43 dysfunction, aiming to identify mechanisms relevant across multiple neurodegenerative conditions. Loss-of-function models effectively mimic the pathological effects of TDP-43 aggregation [ 82 – 84 ], and in fact, recent studies have shown that inducing TDP-43 aggregation leads to loss of nuclear TDP-43 [ 85 , 86 ]. Notably, TDP-43 depletion from microglia has been shown to exacerbate neuroinflammation, pointing to the importance of studying TDP-43 pathology in non-neuronal cells [ 87 ]. In our study, we achieved significant nuclear depletion of TDP-43 using shRNA knockdown in monocyte-derived microglia-like cells (MDMi). Unlike patient-derived MDMi in previous studies, our model did not exhibit cytoplasmic aggregation or phospho-TDP-43, suggesting that nuclear depletion alone is insufficient for these pathological features but still drives significant metabolic and immune alterations. Our data demonstrates a clear link between TDP-43 nuclear depletion and triglyceride (TG) alterations that contribute to lipid droplet (LD) accumulation and altered immune responses in MDMi. While previous studies have demonstrated that microglia LD accumulation can occur via upregulation of TG pathways [ 63 ], this has not been shown in the context of ALS. Excessive LD accumulation could be a consequence of several factors, and their varying composition can affect functional outcomes. In a study using both human AD brain tissue and iPSC-derived microglia with different ApoE genotypes (ApoE3/E3 vs ApoE4/E4), microglia with ApoE4/E4 genotype had increased LDs, greater expression of ACSL1 when exposed to fibrillar Aβ and an induction of TG synthesis. These cells were also found to express markers of “cellular senescence” and exhibit less phagocytosis [ 25 ]. Here, we found that TG-driven LDs in fact result in increased phagocytic activity, which points to heterogeneity in the effects of LD accumulation. Several genes associated with fatty acid metabolism, including ACSL1 , FABP4, ELOVL3, and ACSL4 , were upregulated in our knockdown. In addition to the involvement of ACSL1 with LD-accumulating microglia [ 25 , 88 ], some of these genes have also been linked to lipid dysregulation and ALS. IPSC-derived microglia with mutations in profilin-1(PFN1) that are causative for ALS were shown to exhibit upregulated FABP4 and FABP5 [ 89 ]. Other studies have linked FABP4 to increased TG synthesis and LD accumulation [ 40 ]. ELOVL3 was found to be significantly increased in LPL knockdown microglia, which also exhibit increased LD formation, reduced cholesterol synthesis and efflux, and increased inflammation [ 44 ], which is in line with our measured phenotypes. In our knockdown, these genes were downregulated with inhibition of DGAT 1 and 2 enzymes, suggesting that inhibiting triglyceride synthesis triggers a feedback mechanism that downregulates fatty acid uptake/metabolism pathways. Metabolic alterations and bioenergetic shifts are well-documented in ALS, with many studies reporting a transition from glycolysis to fatty acid oxidation, particularly in glycolytic tissues such as skeletal muscle [ 90 – 92 ]. Recent findings from our group [ 93 ] demonstrated that motor neurons from ALS SOD1 mutant mice exhibit upregulation of glucose, fatty acid, and amino acid catabolism, with impaired oxidative phosphorylation (OXPHOS) and increased fatty acid oxidation. In our current study, we found significant downregulation in the expression of PFKP and G6PD , both critical for glycolysis. Prior studies have linked TDP-43 to PFKP regulation, with TDP-43 loss-of-function reducing PFKP expression and activity, potentially through cryptic exon inclusion [ 50 , 94 – 96 ]. Our findings align with these reports, supporting a model in which loss of nuclear TDP-43 leads to impaired glycolysis and a shift toward fatty acid uptake and utilization. TARDBP knockdown resulted in notable changes in immune-related gene expression. TREM2 and IL10 were significantly downregulated, while TREM1 , IL1Β , and CCL4 were upregulated. The opposing roles of TREM1 and TREM2 in inflammation suggest that loss of nuclear TDP-43 skews microglia toward a pro-inflammatory state [ 16 , 97 , 98 ]. Interestingly, TREM2 is upregulated in DAM and microglia associated with neurodegeneration, but not in LDAM (lipid droplet-accumulating microglia), which are thought to be associated with aging and Alzheimer’s Disease [ 24 ]. Although sequencing studies have shown an upregulation of TREM2 (along with other DAM genes) in ALS microglia, it is known that TREM2 genetic variation (such as in the R47H variant) is also associated with ALS. Additionally, TREM2 is required for the protective role in attenuating the expression of pro-inflammatory mediators, including iNOS, TNFα, IL-1β, and IL-6, as well as mediating phagocytosis of TDP-43 aggregates in the context of ALS. Our data demonstrating a decrease in soluble TREM2 protein therefore supports defects in immune responses in TARDBP knockdown MDMi. The observed increase in expression of CCL4 , which encodes a chemokine that correlates positively with better ALS functional scores [ 99 ], suggests a possible early-stage protective response that could become detrimental over time. Notably, treatment with DGAT inhibitors reduced IL1Β , CCL4 , and TREM1 expression, but did not alter TREM2 , linking TG accumulation specifically to pro-inflammatory pathways (21,93). Treatment with DGAT inhibitors also significantly reduced NLRP3 , and IL18 gene expression, and more importantly, IL-1β protein levels, implicating the inflammasome in the inflammatory phenotypes observed in the TARDBP knockdown. The role of LD composition in microglia function remains poorly understood. While TREM2-deficient microglia accumulate CE-rich LDs, which can be rescued by ACAT1 inhibitors [ 100 ], our data suggest that TDP-43 deficiency leads to TG-driven LD accumulation, which cannot be rescued by ACAT1 inhibitors. Although we do see a decrease in soluble TREM2 protein levels in our model, our LD phenotype is driven by triglycerides rather than cholesterol esters. Notably, we found that inhibiting ACAT1 reduced LDs in the scramble control, whereas inhibiting DGAT1 and 2 reduced LDs in the knockdown, suggesting that TDP-43 depletion leads to a shift in LD composition in MDMi. Here we observe a distinct phenotype where LD accumulation is accompanied by increased phagocytosis, unlike other studies that have shown impaired phagocytosis in LD-accumulating microglia [ 24 , 25 , 100 , 101 ]. Some studies have also demonstrated the importance of LD accumulation in driving anti-inflammatory responses in microglia [ 102 , 103 ]. This highlights the fact that heterogeneity in LD-associated phenotypes needs further characterization in the context of neurodegenerative diseases involving lipid alterations. In our model, nuclear depletion of TDP-43 could be driving TG-mediated inflammatory pathways, which increase baseline activation of MDMi and increase phagocytic activity. Our lipidomic analysis supports the observed phenotypes, as we found several unsaturated TGs to be elevated in the TARDBP knockdown. These were primarily TGs containing oleic acid (18:1), which are known to accumulate in neutral lipid droplets. Interestingly, previous studies have shown accumulation of oleic acid (OA18:1) in a model of Parkinson’s Disease [ 104 , 105 ], and macrophages are known to accumulate unsaturated TGs in the “M1 polarization state”, associated with inflammation [ 106 ]. Acyl carnitines (ACs) are fatty acids that are transported to mitochondria for beta-oxidation, and alterations are an indication of mitochondrial dysfunction. The decrease in long-chain ACs (C12:0) and corresponding increase in short-chain ACs (AC 3:0) suggests increased fatty acid oxidation. This could also potentially implicate incomplete fatty acid oxidation as a consequence of excessive fatty acid uptake for ATP production in a state of impaired glycolysis (supported by the downregulation of glycolytic genes in our Fluidigm data). Accumulation of short-chain ACs is also associated with inflammatory responses and oxidative stress in a variety of metabolic contexts [ 107 ], and alterations in short-chain ACs have been found in neurodegeneration and aging [ 108 , 109 ]. Finally, we found reductions in BMPs and lactosylceramides, and an increase in mono hexosyl ceramides, both of which are an indication of impairments in lysosomal function and lipophagy [ 110 – 112 ]. This could imply an alternate mechanism of TG accumulation that results from defects in lysosomal degradation of lipids, rather than increased de novo synthesis. Overall, our findings suggest a TARDBP -specific metabolic shift, characterized by reduced cholesterol biosynthesis and uptake, coupled with a possible impairment in glycolysis, resulting in increased fatty acid uptake and TG accumulation. The observed TG alterations are not unique to our study and have been found in ALS patient serum, Parkinson’s Disease iPSC-motor neurons, as well as ApoE4 microglia [ 63 , 113 – 116 ], although AD lipidomic studies have found more drastic alterations in phospholipids, sphingolipids, and cholesterol esters [ 117 – 120 ]. However as described above, the LD phenotype observed in our TDP-43-depleted MDMi is unique in its effect on microglia function, and this could be explained by bioenergetic alterations that specifically affect glycolysis and fatty acid metabolism in these cells. Interestingly, a previous study using TARDBP knockdown in iPSC-derived motor neurons and HeLa cells found reduced ATP-linked respiration and impaired mitochondrial function, reduced PFKP expression, and no alteration in TGs [ 50 ]. While our data also show reduced PFKP expression, the increase in TGs appears microglia-specific, suggesting unique metabolic adaptations across cell types. Additional studies examining substrate utilization in TARDBP knockdown microglia and patient-derived microglia will be crucial in elucidating the mechanisms underlying TG heterogeneity. Lastly, we were able to show that MDMi derived from individuals with TARDBP mutations recapitulated some key phenotypes of our TARDBP knockdown MDMi, including LD accumulation, increased IL1β, and response to DGAT inhibitors. This provides an exciting, new understanding of metabolic alterations that could drive ALS pathology in a TDP-43-dependent manner. MDMi derived from sALS patients also displayed significant increases in IL1β and accumulation of large LDs, although mean LD intensity was reduced. 90% of sALS patients exhibit TDP-43 pathology, and these phenotypes could therefore be driven by TDP-43 to some extent. However, gene expression analysis showed higher expression of fatty acid and triglyceride metabolism genes in the TDP-ALS MDMi compared to sALS MDMi, specifically, DGAT2 was increased in the TDP-ALS MDMi and not sALS MDMi (in addition to FABP4 , FADS1 , FASN , and ACSL4 ) although only FASN was significant (p-value < 0.05). Measuring TDP-43 nuclear depletion by immunohistochemistry in TDP-ALS compared with sALS MDMi would enable a better understanding of whether the differences are driven by TDP-43 nuclear depletion. It is important to note that although DGAT inhibitors reduced LipidTox intensity, they did not reduce LD size in TDP-ALS MDMi, suggesting that while blocking DGAT enzymes may reduce the number of LDs, the existing triglycerides may be stored in fewer LDs, making them bigger. Additionally, DGAT1 is thought to be involved in the formation of new LDs, while DGAT2 is involved in expansion of existing LDs [ 64 , 121 ]. The inhibitors may have a disproportionate effect on these enzymes, causing greater inhibition of DGAT1 compared to DGAT2. Interestingly, other immune-related genes like TREM2 and CCL4 showed distinct alterations in the TDP-ALS MDMi compared to the knockdown. TREM2 was slightly upregulated, while CCL4 was downregulated. This could be due to a difference in the stage of disease captured by both models or could reflect heterogeneity in ALS subtypes. When comparing to the shRNA knockdown, genes related to carbohydrate metabolism and cholesterol biosynthesis were both increased in the TDP-ALS MDMi, which contrasts with the TARDBP knockdown MDMi. This could be a compensatory effect of reduced function or suggest alternate mechanisms of lipid dysfunction in both models. Functional assays to measure glycolysis and cholesterol synthesis would therefore be required to elucidate how the knockdown model differs from patient-derived MDMi and the extent to which gene expression may be correlated with phenotypes such as TG and cholesterol levels in these cellls. Our study has certain limitations to be acknowledged. While several methods exist to model human microglia, we used MDMi, which undergo a similar polarization step as iPSC-derived microglia, but are more adept at incorporating human variability driven by age, disease state, and natural heterogeneity [ 122 , 123 ]. Although this heterogeneity reflects real-world diversity, future studies should employ a larger sample size to determine whether there are genotype-specific effects. Further, all our analyses were performed at Day 14 post-differentiation, which may represent a late-stage response to TARDBP knockdown since the shRNA treatment is done on Day 4. Additionally, the observed increase in ATGL and HSL expression and decrease in DGAT1 and DGAT2 expression in the knockdown model, despite elevated triglycerides, suggests that microglia may attempt to counteract triglyceride accumulation over time. Time-course experiments could clarify whether early-stage metabolic adaptations differ from later responses. Our targeted gene expression approach with Fluidigm analysis, while informative, does not capture the full transcriptomic landscape. Unbiased RNA sequencing could provide a more comprehensive view of TARDBP knockdown effects and also uncover important differences between the shRNA knockdown model and the ALS patient-derived MDMi model. Additionally, lipidomic analysis of isolated lipid droplets from a greater sample size to understand the full profile of triglyceride, cholesterol and fatty acid species would help validate some of our data. With regards to the use of shRNA-mediated lentiviral knockdown, while this is effective and provides a very stable knockdown allowing for downstream assays, alternative approaches such as siRNA or CRISPR-mediated knockdown in iPSC-derived microglia could further validate our findings. Our knockdown reduced protein levels by 20–30%, which is low compared to other studies that use iPSC-derived microglia or cell lines that allow for more efficient knockdown, however, even with a modest knockdown, we do see a large biological effect. Nevertheless, using these alternate approaches would also provide larger cell counts to perform activity assays and western blots to confirm gene expression data. Lastly, while we were able to obtain three TDP-ALS and nine sALS patient samples, cell yields were insufficient to perform nuclear TDP-43 staining or functional assays such as phagocytosis and response to LPS stimulation. Future studies with a larger cohort of patient-derived samples will be essential to fully elucidate how lipid pathways are altered in ALS and how these changes impact immune function. RNA sequencing and splicing analysis would also potentially uncover novel targets by which TDP-43 could directly modulate bioenergetic and triglyceride pathways in microglia. Conclusion Our study demonstrates that nuclear depletion of TDP-43 in MDMi leads to metabolic and immune alterations, characterized by increased fatty acid uptake, triglyceride accumulation, and possible impairment in glycolysis. These changes are accompanied by pro-inflammatory cytokine production, independent of cytoplasmic TDP-43 aggregation. The findings highlight the importance of microglial lipid metabolism in neuroinflammation and suggest that triglyceride accumulation may drive microglia activation in ALS. Further studies on lipid droplet heterogeneity and metabolic adaptations in microglia could provide new insights into ALS pathogenesis and identify potential therapeutic targets. Materials and Methods MDMi Cell Culture : MDMi are created as described previously [ 124 ]. Blood from healthy human donors is separated using a density gradient medium Lymphoprep (Stemcell technologies #07851) to isolate mononuclear cells. These peripheral blood mononuclear cells (PBMCs) are cryopreserved in Fetal Bovine Serum (FBS) with 10% Dimethyl sulfoxide (DMSO) at -80 o C until needed. PBMCs are thawed and monocytes are obtained through CD14 + microbead isolation (Miltenyi #130-050-201). Monocytes are then plated in 96 well plates at a density of 200,000 cells per well and cultured in serum-free RPMI (Gibco #R8758) media with 1% penicillin and streptomycin (10,000 U/mL) (Fisher Scientific 15-140-122) and 2.5 µg/mL Fungizone (Cytiva #SV30078.01). A cytokine cocktail consisting of macrophage colony-stimulating factor (M-CSF) (10 ng/mL), granulocyte-macrophage colony-stimulating factor (GM-CSF) (10 ng/mL), nerve-growth factor-b (NGF-b) (10 ng/mL), chemokine ligand 2 (CCL2) (100 ng/mL), and interleukin-34 (IL-34) (100 ng/mL), is added to the media. Cells are differentiated into microglia-like cells (MDMi) over 10 days with the help of these cytokines. Cytokines were purchased from R&D Systems (NGF-b, GM-CSF, and IL-34) and Biolegend (M-CSF and CCL2). Monocyte-derived microglia-like cell models have been reviewed and characterized as an appropriate model to study human microglia in vitro [ 35 , 124 – 127 ]. Assays were done using MDMi from 3–6 individuals per experimental run. For each assay, 2 to 4 batches (repeat experimental runs) were done and the total number of individuals used per assay is in the figure legend. Preparation of shRNA lentiviral particle : Lentiviral particles were prepared as previously described [ 128 , 129 ]. Briefly, on day 1, 293T cells were transfected using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA, United States) with packaging and envelope plasmids (Vpx cDNA and pHEF-VSVG). On day 2, 293T culture media was replaced with RPMI-1640 Glutamax (Invitrogen, Waltham, MA, United States) containing 1% fungizone (Amphotericin B) and 1% penicillin/streptomycin. After 48 hours, lentiviruses containing the Vpx particles were harvested, centrifuged for 5 minutes at 400xg and the supernatant collected. The supernatant was filtered using a 0.45-µm syringe filter (EMD Millipore, Burlington, MA, United States). Lentiviral particles containing targeted shRNA for each gene were obtained from Milipore Sigma (TARDBP construct: TRCN0000016038, Target Sequence: GCTCTAATTCTGGTGCAGCAA). Lentiviral mediated knockdown of MDMi : For the transduction of MDMi cells, on day 4 of differentiation, the culture media was replaced with 100 µl of Vpx-VLP and 100µL of fresh RPMI media containing 2X concentration of cytokines. After 2–3 hours, 10 µl TRC virus-containing shRNA or scramble control (Sigma) was added to each well. On day 7, puromycin (Life Technologies, Carlsbad, CA, United States) at a concentration of 3 µg/ml was added to eliminate non-transduced cells. On day 10, MDMi were lysed for RNA isolation [ 128 , 129 ]. A > 50% knockdown of RNA (by qPCR) was considered optimal for the experiment (higher knockdown efficiencies could not be obtained for the TARDBP gene, and this level of knockdown showed significant protein level reduction, so it was considered sufficient to study effects of TARDBP reduction). The knockdown was performed with PBMCs from 20 individuals (11 male, 7 female, and two unknown, aged 18 to 70). Of these, 5 samples were removed from analysis due to insufficient knockdown (< 50%). Information on the age, sex and ethnicity of these individuals are provided in Supplementary Table 1 . Cholesterol uptake, fatty acid uptake and lipid droplet staining : A fluorescent cholesterol analog, NBD-cholesterol (22‐(N‐(7‐Nitrobenz‐2‐oxa‐1,3‐Diazol‐4‐yl)Amino)‐23,24‐Bisnor‐5‐Cholen‐3β‐OI) (ThermoFisher N1148) was used to determine cholesterol uptake. Cells were incubated with NBD-cholesterol for 1 hour, followed by fixing with 4% paraformaldehyde (PFA). Fluorescence was quantified using images from confocal microscopy or by plate reader measurements (TECAN Infinite 200 PRO). Fatty acid uptake was measured by incubating cells in a similar manner with 20 µM C-12 BODIPY labeled fatty acid (ThermoFisher D3823). Staining of lipid droplets was performed using HCS LipidTox™ Deep Green or Red neutral lipid stain (ThermoFisher H34475) according to manufacturer instructions. Zeiss LSM 900 was used for confocal microscopy and images were quantified using CellProfiler. For plate reader measurements, 16 readings were taken per well covering different areas within the well, and mean values were plotted. Total Cholesterol/Free cholesterol Assay : Total and free cholesterol levels were determined using the Amplex Red cholesterol assay (ThermoFisher A12216) as per the manufacturers protocol. Cells were grown on 12-well plates, and after the knockdown protocol on day 10, media containing viral particles was replaced with fresh RPMI media without cytokines. After 3 days, cells were collected using a cell scraper, and frozen at -80 o C in 250 µL water. Supernatants were also collected and frozen. When ready to perform the assay, cells were thawed, and the protein was quantified using Bradford’s assay. Lipid extraction was done by adding chilled chloroform and methanol in a 2:1 ratio. This was followed by vortexing for 30 seconds and 2-minute incubation on ice, repeated 3 times. The samples were then centrifuged at 14000rpm for 10 minutes, followed by transferring the lower organic layer to a new Eppendorf tube. This was placed under nitrogen until all the chloroform was dried, followed by resuspension of the lipids in the reaction buffer provided in the kit. Cholesterol esters were calculated by subtracting free cholesterol from total cholesterol. All values were normalized to protein levels measured with Bradford’s assay. Triglyceride/Glycerol Assay : Triglyceride-Glo Assay (Promega J3160) was used to measure total glycerol and free glycerol in cells and supernatants, as per the manufacturer’s instructions. Cells were seeded in 96-well plates in duplicate (to perform the assay with and without lipase). On Day 10, media containing viral particles was replaced with fresh RPMI media without cytokines. After 3 days incubation in fresh RPMI, on Day 14, the supernatant was removed and collected, and cells were washed once with PBS, followed by the addition of the glycerol lysis buffer (as per kit instructions). Cells and supernatants were assayed at the same time, and luminescence was measured by a plate reader. Triglyceride levels were calculated by subtracting free glycerol from total glycerol. An equal number of cells plated per well serves as normalization, as protein quantification could not be performed with this kit. Immunohistochemistry : MDMi were plated in 24-well plates with glass coverslips. On Day 10, cells were washed 3X with 3% BSA and 0.1% TritonX in PBS, fixed with 4% PFA for 15 minutes, washed again, and incubated with primary antibodies (Goat Anti-Iba1, Fujifilm CAT# 011-27991 and Rabbit Anti-TDP43, R&D Systems, CAT# MAB7778, Rabbit Anti- phospho-TDP43, Proteintech, CAT#22309-1-AP) overnight at 4 o C. The next day cells were washed 3X with PBS followed by incubation with the appropriate Alexa-conjugated secondary antibodies for 1 hour at RT. Cells were washed again 3X, and the glass coverslips were then mounted on microscope slides using FluoroG mounting solution and imaged at 60X using Zeiss LSM900 confocal microscopy. Western Blot Analysis : Cell protein concentrations were measured using the Quick Start Bradford Protein Assay Kit 1 (Bio-Rad 5000201) in a Tecan Infinite F200 PRO spectrophotometer. 10 µg protein was combined with 4X Laemmli loading buffer in a final volume of 30 µL, heated at 95 o C and loaded on an Invitrogen 4–20% tris–glycine SDS-PAGE gel. Electrophoresis was conducted at 80–120 V using standard tris–glycine running buffer. The sample was transferred to an Immuno-Blot PVDF membrane (Bio-Rad 1620177) in standard tris–glycine transfer buffer with 20% methanol and 0.04% SDS at 150 mA for 2 h in a wet transfer. The primary antibodies used are as follows: Anti-TDP43, R&D systems, CAT# MAB7778, Anti-GAPDH Cell Signaling, CAT#5174S. Dextran Uptake Assay : Cells were incubated with 0.1 mg/mL Dextran Alexa Fluor™ 647 10,000 MW (ThermoFisher D22914) for 1 hour at 37 o C, followed by fixation with 4% PFA and imaging with a confocal microscope. As a positive control, cells were incubated with 10µM Cytochalasin D (FisherSci., Cat# 12–331) for 20 minutes prior to incubation with Dextran. Image analysis was done using CellProfiler. Cell Lysis and RNA Isolation : MDMi are lysed with RLT buffer (Qiagen #74104) with 1:100 β-mercaptoethanol, purified RNA is extracted using the RNeasy 96 well plate isolation kit (Qiagen #74182). Reverse Transcriptase PCR and qPCR: RNA is transformed to cDNA using reverse transcription PCR. The PCR mix consists of dNTP mix (#R72501), Random Hexamers (#N8080127), RNase Inhibitor (#N8080119), MgCl 2 (#AB0359), 10x PCR buffer (#4486220), and M-MLV Reverse Transcriptase (#28025013) purchased from Thermofisher Scientific. Reagents and RNA are loaded into a 96-well plate to a total volume of 50µL and run in an Applied Biosystems MiniAmp Thermocycler. Thermocycler program: 25 o C for 10 minutes, 48 o C for 45 minutes, 95 o C for 5 minutes, and held at 4 o C upon run completion. Sample cDNA is loaded into a 96-well PCR plate with Taqman Fast Advanced Mastermix (#4444554), assay primer for the target gene to be detected with FAM, and housekeeping gene to be detected with VIC. qPCR primers were purchased from Thermofisher Scientific. For all experiments, the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used to normalize all values as relative expression (R.E.). Samples are run with the protocol: Stage 1 (x1): 50 o C for 2 minutes, 95 o C for 2 seconds. Stage 2 (40x): 95 o C for 1 second, 60 o C for 20 seconds. Cycle threshold (Ct) values are collected and normalized to GAPDH Ct. Microfluidic qPCR Analysis : Gene expression analysis was performed by parallel qPCR using the high-throughput Fluidigm BioMark HD platform (Standard BioTools, San Francisco, CA, USA), according to the manufacturer's instructions. GAPDH and TUBB were used as reference genes, but final data are reported using GAPDH as a reference. A pre-amplification step was included to increase the number of cDNA copies to a detectable level and to allow the concurrent amplification of the different gene expression targets. The Fluidigm IFC was primed with control line fluid on the IFC controller. Subsequently, assay and sample mixes were loaded on the IFC and placed on the controller which pressure-loaded the assay components into the reaction chambers. The IFC was then placed on the Biomark HD for thermocycling and fluorescence detection. The data was reviewed on the Fluidigm Real Time analysis software. Data from multiple Fluidigm runs was normalized by calculating fold change of the knockdown over scramble control. Multiple paired t-test was used to determine significance of alterations, and this was used to make a volcano plot. Although Fluidigm analysis can only analyze 96 genes simultaneously, our data includes 110 genes, as multiple Fluidigm runs were combined, and not all genes were run on every sample. Confocal Microscopy : Cells were imaged on the Zeiss LSM 900 confocal microscope. Images were processed using FIJI image processing software and analyzed using the open-source program CellProfiler to classify and count cells and measure staining intensity. For CellProfiler, customized pipelines were developed to analyze lipid droplet intensity, TDP-43 localization and dextran uptake [ 66 ]. Compactness was measured using the “MeasureObjectShapeSize” module, where compactness is calculated as “the mean squared distance of the object's pixels from the centroid divided by the area”. A filled circle will have a compactness of one, whereas flatter or more elongated and ramified cells will have a compactness greater than one. CellProfiler Pipelines : Imaging analysis pipelines involved identifying cells as a primary object using DAPI stain, setting appropriate thresholds for the nucleus size. CellMask staining was used to identify the cell body as a secondary object. Cells were filtered to only include those with a nucleus (using “FilterObject” and “RelateObject” modules), followed by measuring the average intensity per cell using “MeasureObjectIntensity” for LipidTox staining, NBD-cholesterol, or Dextrain staining. To measure TDP-43 nuclear and cytoplasmic staining, Hoechst staining was used to identify the nucleus, and Iba1 staining was used to identify the cell body. The “MaskObject” module was used to identify cytoplasmic area (without nucleus). Mean values per image were used for final analysis in all imaging analysis except ALS patient-derived MDMi LD analysis, where a nested analysis was performed. For each well, 5–15 images were obtained and analyzed, with each image containing 20–100 cells. CellMask Stain : For lipid droplet staining and dextran uptake assays, CellMask (Invitrogen CAT#C10045) was used to stain the plasma membrane before fixing cells. Briefly, cells were incubated with CellMask (1:1000) for 5–10 minutes at 37 o C in RPMI media, followed by washing 3X with PBS and fixing with 4% PFA. Drug Inhibitors : DGAT 1 and 2 inhibitors (Sigma PF04620110 and PF06424439 respectively) were added simultaneously at a concentration of 5uM. To inhibit cholesterol esterification, 20 µM 3-[Decyldimethylsilyl]‐N‐[2‐(4‐methylphenyl)‐1‐phenethyl] propanamide (Sandoz 58‐035), a specific ACAT1 inhibitor, was used. To inhibit fatty acid uptake via CD36 inhibition, we used Sulfosuccinimidyl Oleate (sodium salt), or SSO (Cayman Chemical # 1212012-37-7) at a concentration of 20 µM. For all drug treatments, MDMi media was changed on day 10 (after lentiviral knockdown) to remove virus from the media. After 3 days, inhibitors were added for 12–16 hours, and the next day (day 14) cells were assayed. DMSO was used as the vehicle control. Lipidomic Analysis: Samples were sent to the Lipidomics Core facility at Columbia University Medical Center. Lipids were extracted from equal amounts of material (1 million cells per sample). Lipid extracts were prepared via chloroform–methanol extraction, spiked with appropriate internal standards, and analyzed using a 6490 Triple Quadrupole LC/MS system (Agilent Technologies, Santa Clara, CA) as described previously [ 130 ]. Statistical Analysis : Data are expressed as mean ± SEM with statistical significance being determined as P values generated with a 95% confidence interval. All paired t-tests were 2-tailed, assuming normal (Gaussian) distribution. Multiple paired t-tests were performed for Fluidigm data analysis of knockdown MDMi, and an adjusted p-value of < 0.05 (correcting for multiple comparisons using the Holm-Sidak test) was considered significant. Unadjusted p-values from multiple unpaired t-tests (2-tailed, Gaussian distribution) were used to analyze Fluidigm data for ALS patient-derived MDMi due to low sample numbers. DGAT-treated samples were compared to untreated samples using paired analyses. Statistical analysis on lipidomics data was performed using 2-tailed paired t-tests. All statistical analysis was performed in GraphPad Prism. To denote significance GP style annotation is used: ns not significant; p < 0.05 (* ), p < 0.01 (**), p < 0.001 (*** ), p < 0.0001 (****). Abbreviations ACAT1: Acyl-coenzyme A:cholesterol acyltransferase-1 AD: Alzheimer’s Disease ALS: Amyotrophic lateral sclerosis DAM: Disease-Associated Microglia DGAT: Diacylglycerol Acyltransferase LD: Lipid Droplet MDMi: Monocyte-Derived Microglia-Like Cells Declarations Acknowledgements The authors are grateful to the participants of the New York Blood Center (NYBC) and the Eleanor and Lou Gehrig ALS Center at Columbia University for their contribution to this research. All participants were recruited and consented to participate and donate samples for research related to neuromuscular conditions under Columbia IRB Protocol # AAAK2000. This work was supported by the US National Institutes of Health grants R21AG073882, RF1AG058852, and R01AG076018 (EMB) and the Department of Defense grant AL200097 (WE). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. EMB and WE are current Ludwig Scholars in the Carol and Gene Ludwig Center for Research in Neurodegeneration. Declaration of Competing Interest The authors have no competing interests to declare. Author Contributions K.K. and E.M.B. implemented the study and wrote the manuscript. E.A.G analyzed and interpreted lipidomics data. D.D. made PCA plots and heatmaps for Fluidigm data analysis. R.T helped with the analysis of confocal imaging data using CellProfiler. 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ElsevierRB Chan, TG Oliveira, EP Cortes, LS Honig, KE Duff, SA Small, MR Wenk, G ShuiJournal of Biological Chemistry, 2012•Elsevier n.d. Additional Declarations No competing interests reported. Supplementary Files Table1NYBCDemographics.jpg Table2ALSMDMiDemographics.jpg Supplemental1WesternBlot.jpg Fig S1: Western blot showing reduced protein levels in TARDBP knockdown A) Western Blot for scramble (Scr) vs TARDBP knockdown (KD) showing TDP-43 band at ~45KDa. Bottom panel shows GAPDH at ~35KDa. Due to smaller TDP-43 bands at a similar size as GAPDH, minimal exposure was used to probe for GAPDH (used for normalization) show in panel below. B) Quantification of TDP-43 intensity normalized to GAPDH (N=6) C) TDP-43 nuclear to cytoplasmic ratio calculated using confocal imaging data (N=10). Statistical Analysis: paired t-test. Data represent the mean ± SEM; *P < 0.05, **P < 0.01, ***P < 0.001 Supplemental2phosphoTDPstain.jpg Fig S2: Phospho-TDP-43 staining is unchanged in TARDBP knockdown A-C) Mean intensity of nuclear, cytoplasmic and total phospho-TDP quantified by CellProfiler using confocal images (60X) for scramble compared to TDP-43 knockdown MDMi (N=8). D) Nuclear to cytoplasmic ratio of pTDP-43 mean intensity (N=8) E) Confocal images of pTDP-43 staining showing Hoechst and Iba1 (60X). Scale bar = 10μm. Statistical Analysis: Paired t-test. Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental3IL1BinTDPKDTREM2ELISA.jpg Fig S3: IL1B expression and protein levels are reduced in TARDBP knockdown MDMi A) IL1B gene expression measured by qPCR in scramble versus TDP-43 KD MDMi (N=24). B) IL1β protein quantification measured by ELISA in scramble versus TDP-43 KD MDMi (N=10) C) Soluble TREM2 protein quantification measured by ELISA in scramble versus TDP-43 KD (N=6). Statistical Analysis: Paired t-test. Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental4NBDuptakeandFCtoTCratios.jpg Fig S4: NBD-cholesterol uptake is not significantly upregulated in TARDBP knockdown stimulated with LPS compared to control A) NBD-cholesterol mean fluorescence intensity (MFI) in scramble versus scramble+LPS (N=30). B) NBD-cholesterol MFI in TDP-43 KD compared to TDP-43 KD+LPS (N=25) C) Ratio of Scramble+ LPS:Scr versus TDP KD+LPS:TDP KD (N=25). D) Free cholesterol: Total cholesterol ratio in scramble versus TDP KD (N=8) E) Ratio of SUPs:Cells of total cholesterol in scramble versus TDP-43 KD (N=8). F) Mean Lipidtox intensity in scramble vs TDP-43 KD MDMi treated with ACAT inhibitor (N=5). Statistical Analysis: A-E) Paired t-test and F) 2-way ANOVA. Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental5celltosupsratioofTAG.jpg Fig S5: Cell to supernatant ratio of triglycerides is increased in TARDBP knockdown MDMi A-C) Cell to supernatant ratio of Triglyceride, total glycerol, and free glycerol in scramble versus TARDBP knockdown (TDP-43 KD) MDMi (N=11). Statistical Analysis: Paired t-test. Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental6ACATTGassay.jpg Fig S6: ACAT1 inhibitor does not alter triglyceride levels in MDMi A-C): Total glycerol, Free Glycerol, and Triacylglycerol levels in cell lysates of scramble versus TARDBP knockdown (TDP-43 KD) treated with and without ACAT1 inhibitor, measured using Promega TriGlo Assay. D) Total glycerol measured in supernatants of scramble versus TDP-43 knockdown MDMi treated with and without ACAT1 inhibitor (Free glycerol levels were too low to quantify) (N=12). Statistical analysis: 2-Way ANOVA. Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental7TGgeneexpressionandTDPexpressionwithDGATACAT.jpg Fig S7: qPCR validation of expression of triglyceride-associated genes A-D) Gene expression measured using TaqMan qPCR in scramble versus TARDBP knockdown (TDP-43 KD) MDMi (N=15). Statistical Analysis: Paired t-test. Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental8HeatmapswithDGAT.jpg Fig S8: DGAT inhibitor significantly alters gene expression in control and TARDBP knockdown MDMi Heatmaps showing Fluidigm data for gene expression alterations in control vs TARDBP knockdown (KD), along with effect of DGAT inhibitor on control and TARDBP knockdown. Fold change is expressed as Log2FC. Genes that were significantly altered (p-value<0.05) are indicated by the black dot and grey dot indicates genes that were close to significance. Statistical analysis: Multiple paired t-test. Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 FigS8TDPKDwithDGATinhfluidigmheatmappapersubmission.xlsx Supplemental9SSOdata.jpg Fig S9: Fatty acid uptake is altered by CD36 inhibition, but lipid droplets are not A) C12-BODIPY mean intensity measured by CellProfiler using confocal images for scramble and TDP-43 KD MDMi treated with DGAT inhibitors overnight (N=4). B) C12-BODIPY mean intensity measured by CellProfiler using confocal images, for scramble and TDP-43 KD MDMi treated with SSO for 4 hours (N=7). C) LipidTox mean intensity measured by CellProfiler using confocal images for scramble and TDP-43 KD cells +/- SSO for 24 hours (N=5). D) LipidTox mean intensity for scramble vs KD treated with SSO for 24 hours (N=8). Statistical Analysis: 2 Way ANOVA. Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental10LipidomicsExtended.jpg Fig S10: Lipidomic analysis A-E) Heat maps showing lipidomic analysis of certain species: A) Acylcarnitines B) Triglycerides C) Monohexylceramides D) Bis(monoacylglycero)phosphate, and E) Lactosylceramides. Bold species were significant (paired t-test, p<0.05, N=4). Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental11TDPALSPatientMDMiLipidTox.jpg Fig S11: ALS patient-derived MDMi demographics and mean lipid droplet quantification A) Demographics of TDP-ALS mutant patients and age and sex matched control samples used to obtain PBMCs for MDMi. B) TARDBP gene expression measured by TaqMan qPCR in control vs TDP-ALS mutant patients (N=3). C) Confocal images showing LipidTox staining in controls and TDP-ALS mutant MDMi with DGAT inhibitor treatment. D) Quantification of mean LipidTox intensity using imaging data from CellProfiler in control and TDP-ALS MDMi +/- DGAT inhibitors. E) Quantification of mean lipid droplet (LD) area by CellProfiler in scramble and TDP-ALS MDMi +/- DGAT inhibitors. F) Quantification of mean lipid droplet (LD) radius using CellProfiler in scramble and TDP-ALS MDMi +/- DGAT inhibitors. G) Confocal Images showing LipidTox staining in control vs sALS patient MDMi. H) LipidTox mean intensity in control and sALS MDMi quantified using CellProfiler (N=4). I) Mean lipid droplet (LD) area in control and sALS MDMi quantified using CellProfiler (N=3). Statistical analysis D-F) 2-Way ANOVA, B, H, I) Unpaired t-test. * P < 0.05, ** P < 0.01, *** P < 0.001 Supplemental12PCAplotsandclusteredheatmapofALSpatientFluidigm.jpg Fig S12: TDP-ALS samples cluster together whereas sporadic ALS and controls are heterogenous. A) Clustered heat map using z-score matrix and row clustering for controls, TDP-ALS and sALS samples. B) PCA plot for ALS-TDP and matched controls (N=3), and C) PCA plot for sALS and matched controls (N=6). D-E) Volcano plots showing Log2FC versus -Log10P for Fluidigm gene expression data in ALS-TDP MDMi compared to controls and sALS MDMi compared to controls. Log2FC calculated using average expression values for each group. Statistical test: unpaired multiple t-test (unadjusted p-values). Data represent the mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001. FigS12ALSfluidigmvolcanoplotTDPmutantpapersubmission.xlsx FigS12sALSfluidigmvolcanoplotpapersubmission.xlsx GraphicalAbstract.docx Fig2VolcanoPlotScrvsTDP43KD101025papersubmission.xlsx Fig9TDP43KDLipidomicsdatapapersubmission.xlsx Fig11ALSpatientMDMiheatmapsfinal052925papersubmission.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":390705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown results in TDP-43 nuclear depletion in MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Expression of \u003cem\u003eTARDBP\u003c/em\u003ein knockdown (TDP-43 KD) samples as a percentage of the scramble control (Scr) (N=15) measured using qPCR. \u003cstrong\u003eB\u003c/strong\u003e) 63X Confocal imaging showing staining for Iba1, Hoechst and TDP-43; the last panel shows a merge. Scale bar = 10μm. \u003cstrong\u003eC\u003c/strong\u003e) Quantification of nuclear TDP-43 stain from confocal images, performed using CellProfiler (N=12). \u003cstrong\u003eD\u003c/strong\u003e) Quantification of cytoplasmic TDP-43 stain from confocal images, performed using CellProfiler (N=12).\u003c/p\u003e\n\u003cp\u003eEach connected line shows paired samples from the same donor. Statistical analysis: Paired t-test (**** = p\u0026lt;0.0001)\u003c/p\u003e","description":"","filename":"Figure1TARDBPKDvalidations.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/607a5bd53f54ae16f36173db.jpg"},{"id":96377286,"identity":"4034defc-9f9e-4351-a6f3-bf92fdaf2ec4","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1297610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown significantly alters lipid metabolism gene expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) STRING analysis of Fluidigm genes showing 3 clusters of genes included in the panel: lipid metabolism (red), glycolysis (green) and immune function (yellow). \u003cstrong\u003eB\u003c/strong\u003e) Volcano plot comparing differential gene expression between TARDBP knockdown (TDP-43 KD) and scramble control (Scr)(N=15) with Log2 Fold Change on x-axis and -Log10(adjusted p-value) on y-axis. \u003cstrong\u003eC\u003c/strong\u003e) Table showing most significantly upregulated genes with unadjusted p-values \u0026lt;0.05 \u003cstrong\u003eD\u003c/strong\u003e) Table showing 15 most significantly downregulated genes with adjusted p-values \u0026lt;0.05\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Multiple paired t-test corrected for multiple hypothesis testing using Holm-Sidak method. Adjusted p-values used for volcano plot.\u003c/p\u003e","description":"","filename":"Figure2FluidigmDatawithwashoutusingadjustedpvalues.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/af249336731a24a8ceae2911.jpg"},{"id":96453135,"identity":"ffdda463-020e-4b7c-bbd1-079a2ebdb6ca","added_by":"auto","created_at":"2025-11-21 09:58:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":771687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTDP-43 depletion causes increased lipid droplet accumulation in MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Quantification of LipidTox confocal images using CellProfiler (N= 23) for scramble control (Scr) and \u003cem\u003eTARDBP\u003c/em\u003e knockdown (TDP-43 KD). \u003cstrong\u003eB\u003c/strong\u003e) Confocal images of LipidTox stain comparing scramble control (Scr) and \u003cem\u003eTARDBP\u003c/em\u003e knockdown (TDP-43 KD), 20X. Scale bar = 10μm. \u003cstrong\u003eC\u003c/strong\u003e) NBD-cholesterol uptake mean fluorescence intensity (MFI) quantified by plate reader (N=25). \u003cstrong\u003eD\u003c/strong\u003e) Confocal images of NBD-cholesterol comparing scramble control and TDP-43 KD, 20X. Scale bar = 10μm. Statistical analysis: Paired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure3lipidToxandcholesteroluptake.png","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/3202e80b892b8466088e8175.png"},{"id":96453279,"identity":"95f3d739-3359-49e7-bd55-faa6c0e17024","added_by":"auto","created_at":"2025-11-21 09:59:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":559408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTDP-43 depletion does not alter cholesterol uptake in MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-C\u003c/strong\u003e) Total cholesterol, Free Cholesterol and Cholesterol Ester levels in scramble control (Scr) and \u003cem\u003eTARDBP\u003c/em\u003eknockdown (TDP-43 KD) MDMi (N=8). \u003cstrong\u003eD-F\u003c/strong\u003e) Total cholesterol, Free Cholesterol and Cholesterol Ester levels in supernatants of scramble control (Scr) and \u003cem\u003eTARDBP\u003c/em\u003e knockdown (TDP-43 KD) (N=14) MDMi. All values were normalized using protein concentration calculated by Bradford analysis. Statistical analysis: paired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure4Amplexassaycholesterollevels.png","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/44a76ec30195e0800669c682.png"},{"id":96377294,"identity":"ebfc3053-64ff-4b63-99bf-216268487b6f","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":353175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTDP-43 depletion causes triglyceride accumulation in MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-C\u003c/strong\u003e) Triglycerides, total glycerol and free glycerol levels in scramble control (Scr) vs \u003cem\u003eTARDBP\u003c/em\u003eknockdown (TDP-43 KD) in cell lysates measured using Promega TriGlo Assay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD-F\u003c/strong\u003e) Triglycerides, total glycerol and free glycerol levels in scramble control (Scr) vs \u003cem\u003eTARDBP\u003c/em\u003eknockdown (TDP-43 KD) in supernatants measured using Promega TriGlo Assay. (For cells: Triglyceride only n=24, for Total Glycerol N=35, for Free Glycerol (N=20). For sups: Triglyceride only N=12, Total Glycerol N=35, Free Glycerol N=12). Statistical analysis: paired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. Sample numbers differ for cells vs supernatants due to kit limitations.\u003c/p\u003e","description":"","filename":"Figure5Triglyceridelevels.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/fccff66bc19867b853716a08.jpg"},{"id":96453207,"identity":"be95a66f-a1f4-4927-ae93-7a5a9b7c7cb6","added_by":"auto","created_at":"2025-11-21 09:58:46","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":398062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDGAT1 and DGAT2 inhibitors reduce lipid droplets in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eknockdown MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-C\u003c/strong\u003e) Total glycerol, free glycerol and triglyceride levels in cells, measured using Promega TriGlo Assay (N=14), with (+) and without (-) DGAT inhibitors (DGATinh), in scramble control (Scr) and \u003cem\u003eTARDBP\u003c/em\u003e knockdown (KD). \u003cstrong\u003eD\u003c/strong\u003e) Total glycerol level in supernatants measured using Promega TriGlo Assay (N=12) +/- DGATinh in scramble (Scr) and \u003cem\u003eTARDBP\u003c/em\u003e KD (KD). \u003cstrong\u003eE\u003c/strong\u003e) Mean Lipidtox intensity measured by confocal imaging and quantified by CellProfiler (N=9) in scramble (Scr) and \u003cem\u003eTARDBP\u003c/em\u003eknockdown (KD) +/- DGAT inhibitors (DGATinh). Statistical Analysis: 2-way ANOVA corrected for multiple comparisons using Sidak’s post hoc analysis. Note: Free glycerol levels in supernatants are below kit detection. Total glycerol can be assumed to be triglycerides. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure6DGATinhibitoreffectonTAGandLD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/9687680264bf32695c496248.jpg"},{"id":96453737,"identity":"21ae05b4-24d3-4ff6-8ba3-d31ea69cff63","added_by":"auto","created_at":"2025-11-21 10:01:26","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":278126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTDP-43 depleted MDMi show increased uptake of fatty acids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) 20X Confocal images for BODIPY-C12 uptake with DAPI and CellMask staining in scramble and \u003cem\u003eTARDBP\u003c/em\u003eknockdown (TDP-43 KD), N=12. Scale bar = 20μm. \u003cstrong\u003eB\u003c/strong\u003e) Quantification of BODIPY C12 mean fluorescence intensity (MFI) calculated by CellProfiler. Statistical Analysis = paired t-test, data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure7FattyaciduptakeinKD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/70a976039266333590787245.jpg"},{"id":96453027,"identity":"9b957c8c-741b-4fe2-9bce-7cb410644142","added_by":"auto","created_at":"2025-11-21 09:57:34","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":603743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTDP-43 depleted MDMi have altered morphology and function.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Confocal images for scramble control and \u003cem\u003eTARDBP\u003c/em\u003e knockdown (TDP-43 KD) of MDMi from 3 individuals showing CellMask, LipidTox and DAPI stain. Panel 4 shows the compactness measure in CellProfiler displayed on CellMask stain from Panel 1. \u003cstrong\u003eB\u003c/strong\u003e) Quantification of compactness measured by CellProfiler (N=10) in scramble and TDP-43 KD. \u003cstrong\u003eC\u003c/strong\u003e) Confocal Images of Dextran uptake in scramble and TDP-43 KD, showing CellMask, Dextran and DAPI stain, 10X. Scale bar = 20μm. \u003cstrong\u003eD\u003c/strong\u003e) Mean dextran intensity quantified using CellProfiler (N=10). \u003cstrong\u003eE\u003c/strong\u003e) Compactness measured by CellProfiler in scramble (Scr) and \u003cem\u003eTARDBP\u003c/em\u003e knockdown (KD) +/- DGAT inhibitors (n=9). \u003cstrong\u003eF\u003c/strong\u003e) Mean dextran intensity quantification by CellProfiler in scramble (Scr) and \u003cem\u003eTARDBP\u003c/em\u003eknockdown (KD) +/- DGAT inhibitors (N=9). \u003cstrong\u003eG\u003c/strong\u003e) qPCR for IL1B expression CellProfiler in scramble (Scr) and \u003cem\u003eTARDBP\u003c/em\u003e knockdown (KD) +/- DGAT inhibitors (N=16). \u003cstrong\u003eH\u003c/strong\u003e) qPCR for IL1B expression in scramble (Scr) and \u003cem\u003eTARDBP\u003c/em\u003e knockdown (KD) in MDMi treated with ACAT1 inhibitor. Statistical analysis for B\u0026amp;D: Paired t-test. Statistical Analysis for E-H: 2-Way Anova corrected for multiple comparisons using Sidak’s post hoc analysis. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure8CompactnessDextranuptakeandIL1BinKDwithDGAT.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/5cfa9c2f56bae879eb14544a.jpg"},{"id":96377303,"identity":"c052ff29-f8a2-403f-8b25-ea6c6446090c","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":367276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLipidomic analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown MDMi demonstrates increases in monounsaturated Triglycerides\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBox plot showing significantly altered lipid species in scramble vs \u003cem\u003eTARDBP\u003c/em\u003eKD (TDP-43 KD): \u003cstrong\u003eA\u003c/strong\u003e) Acylcarnitine (AC C12:0) \u003cstrong\u003eB\u003c/strong\u003e) Triglyceride (TG 54:0/18:0) \u003cstrong\u003eC\u003c/strong\u003e) Monoacylglycerol (MG 18:0), and \u003cstrong\u003eD\u003c/strong\u003e) Diacylglycerol (DG 32:0/16:0). \u003cstrong\u003eE\u003c/strong\u003e) Bar chart showing fold change of TG species in knockdown compared to scramble control, with red bars showing decreased species and blue bars showing increased species. The asterisk shows significance (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). Statistical analysis: paired t-test, N=4. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure9Lipidomics.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/c1591296536b8c498995a278.jpg"},{"id":96377309,"identity":"7d1a3155-3a1b-4d43-ae37-1f203e1d0fcc","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":781238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eALS-patient derived MDMi show increased lipid droplets and IL1β, rescued by DGAT inhibition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) \u003cem\u003eIL1B\u003c/em\u003e gene expression in TDP-ALS and matched control MDMi with DGAT inhibitor treatment using qPCR (N=3). \u003cstrong\u003eB\u003c/strong\u003e) IL1β protein quantification in supernatants of TDP-ALS and matched control MDMi using ELISA (N=3). \u003cstrong\u003eC\u003c/strong\u003e) \u003cem\u003eIL1B\u003c/em\u003e gene expression in sALS MDMi and matched controls (N=7) +/- DGAT inhibitor (N=4). \u003cstrong\u003eD\u003c/strong\u003e) IL1β protein quantification in sALS and matched control MDMi supernatants (N=7). \u003cstrong\u003eE\u003c/strong\u003e) Quantification of mean LipidTox intensity for in TDP-ALS and control MDMi +/- DGAT inhibitor treatment (n=3). \u003cstrong\u003eF\u003c/strong\u003e) Quantification of mean LD area in control and TDP-ALS MDMi +/- DGAT inhibitors and \u003cstrong\u003eG\u003c/strong\u003e) Maximum LD radius measured using confocal images for LipidTox in control and TDP-ALS MDMi (N=3). \u003cstrong\u003eH\u003c/strong\u003e) Confocal images for control and TDP-ALS MDMi (20X) showing zoomed in images in the bottom panel with LipidTox and DAPI stain. \u003cstrong\u003eI\u003c/strong\u003e) Quantification of mean LipidTox intensity for control vs sALS MDMi (n=4). \u003cstrong\u003eJ\u003c/strong\u003e) Quantification of mean LD area and \u003cstrong\u003eK\u003c/strong\u003e) Maximum LD radius using confocal images for LipidTox staining of control vs sALS MDMi (N=4). \u003cstrong\u003eL\u003c/strong\u003e) Confocal images for control and sALS MDMi (20X) showing zoomed in images in the bottom panel with LipidTox, CellMask and DAPI stain. Scale bar = 20μm. Note: CellMask stain was not applied in the TDP-ALS samples and their corresponding controls. \u0026nbsp;Statistical Analysis: A-D and F: 2-way ANOVA corrected for multiple comparisons using Sidak’s post hoc analysis E) Nested 2-way ANOVA without posthoc analysis G-K) Nested Unpaired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure10TDPALSmutantpatientdata.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/df1a45637e254f43f31cbd0d.jpg"},{"id":96377297,"identity":"aea4fd15-ba2a-4c90-8a08-a2347dafc908","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":447429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFluidigm analysis of ALS patient-derived MDMi shows distinct alterations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmaps showing Fluidigm data for gene expression alterations in control versus sporadic ALS (sALS) and control versus TDP-mutant ALS (TDP) groups, along with DGAT inhibitor-treated groups. Fold change is expressed as Log2FC, and values that were significant are indicated by the large black dot (p\u0026lt;0.01) and small black dot (p\u0026lt;0.05), while the grey dots indicate close to significance (p=0.05). Statistical analysis for control versus sALS and control versus TDP: multiple unpaired t-test. Statistical analysis for DGAT-treated versus untreated: multiple paired t-test.\u003c/p\u003e","description":"","filename":"Figure11HeatmapsforfluidigmALSMDMiwithDGAT.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/fc75f43d762443f109bed67d.jpg"},{"id":102397132,"identity":"6c11ea12-7700-4e08-ac6f-e6e5ff4a5356","added_by":"auto","created_at":"2026-02-11 10:01:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7917462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/f7d3af85-ac15-4afc-be9d-dc44d8188a6a.pdf"},{"id":96377285,"identity":"a71f8c2f-bba2-4dd5-a764-9499101cfccd","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":606162,"visible":true,"origin":"","legend":"","description":"","filename":"Table1NYBCDemographics.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/d9babafd1081c6c9c720d3f0.jpg"},{"id":96452974,"identity":"93398775-65f5-416e-940d-0bc90f95832b","added_by":"auto","created_at":"2025-11-21 09:56:44","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":376809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Table2ALSMDMiDemographics.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/3204fe516e68dd2bd73259b2.jpg"},{"id":96453613,"identity":"db288ded-6f91-43e4-9871-3750e8a20c6d","added_by":"auto","created_at":"2025-11-21 10:01:02","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":169808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S1: Western blot showing reduced protein levels in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eknockdown\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Western Blot for scramble (Scr) vs \u003cem\u003eTARDBP\u003c/em\u003e knockdown (KD) showing TDP-43 band at ~45KDa. Bottom panel shows GAPDH at ~35KDa. Due to smaller TDP-43 bands at a similar size as GAPDH, minimal exposure was used to probe for GAPDH (used for normalization) show in panel below. \u003cstrong\u003eB\u003c/strong\u003e) Quantification of TDP-43 intensity normalized to GAPDH (N=6) \u003cstrong\u003eC\u003c/strong\u003e) TDP-43 nuclear to cytoplasmic ratio calculated using confocal imaging data (N=10). Statistical Analysis: paired t-test. Data represent the mean ± SEM; *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental1WesternBlot.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/0ea593313446f5b49c0e2dc7.jpg"},{"id":96453228,"identity":"677aca68-c570-48c4-b74a-44bc69f00c08","added_by":"auto","created_at":"2025-11-21 09:58:51","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":289808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S2: Phospho-TDP-43 staining is unchanged in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-C\u003c/strong\u003e) Mean intensity of nuclear, cytoplasmic and total phospho-TDP quantified by CellProfiler using confocal images (60X) for scramble compared to TDP-43 knockdown MDMi (N=8). \u003cstrong\u003eD\u003c/strong\u003e) Nuclear to cytoplasmic ratio of pTDP-43 mean intensity (N=8) \u003cstrong\u003eE\u003c/strong\u003e) Confocal images of pTDP-43 staining showing Hoechst and Iba1 (60X). Scale bar = 10μm. Statistical Analysis: Paired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental2phosphoTDPstain.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/adfebeeb1f446264a0777c9d.jpg"},{"id":96377291,"identity":"3fae0f94-7078-4a59-9f6f-b477cd6261c7","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":133430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S3: IL1B expression and protein levels are reduced in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) \u003cem\u003eIL1B\u003c/em\u003e gene expression measured by qPCR in scramble versus TDP-43 KD MDMi (N=24).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e) IL1β protein quantification measured by ELISA in scramble versus TDP-43 KD MDMi (N=10) \u003cstrong\u003eC\u003c/strong\u003e) Soluble TREM2 protein quantification measured by ELISA in scramble versus TDP-43 KD (N=6). Statistical Analysis: Paired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental3IL1BinTDPKDTREM2ELISA.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/b05b3d2fc3cc7809ac544c7c.jpg"},{"id":96377299,"identity":"0910a700-6011-48af-acab-d72a25d05ff2","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":356725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S4: NBD-cholesterol uptake is not significantly upregulated in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eknockdown stimulated with LPS compared to control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) NBD-cholesterol mean fluorescence intensity (MFI) in scramble versus scramble+LPS (N=30). \u003cstrong\u003eB\u003c/strong\u003e) NBD-cholesterol MFI in TDP-43 KD compared to TDP-43 KD+LPS (N=25) \u003cstrong\u003eC\u003c/strong\u003e) Ratio of Scramble+ LPS:Scr versus TDP KD+LPS:TDP KD (N=25). \u003cstrong\u003eD\u003c/strong\u003e) Free cholesterol: Total cholesterol ratio in scramble versus TDP KD (N=8) \u003cstrong\u003eE\u003c/strong\u003e) Ratio of SUPs:Cells of total cholesterol in scramble versus TDP-43 KD (N=8). \u003cstrong\u003eF\u003c/strong\u003e) Mean Lipidtox intensity in scramble vs TDP-43 KD MDMi treated with ACAT inhibitor (N=5). Statistical Analysis: A-E) Paired t-test and F) 2-way ANOVA. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental4NBDuptakeandFCtoTCratios.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/265f09cefbf196d81b762513.jpg"},{"id":96453214,"identity":"1fd3c4b6-7573-40e5-a44a-5edb5117c5c6","added_by":"auto","created_at":"2025-11-21 09:58:48","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":179687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S5: Cell to supernatant ratio of triglycerides is increased in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eknockdown MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-C\u003c/strong\u003e) Cell to supernatant ratio of Triglyceride, total glycerol, and free glycerol in scramble versus \u003cem\u003eTARDBP\u003c/em\u003eknockdown (TDP-43 KD) MDMi (N=11). Statistical Analysis: Paired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental5celltosupsratioofTAG.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/a5172334e03b7fbb27a338fa.jpg"},{"id":96453366,"identity":"85b3cdd5-ebdb-4a1b-a65c-6a7f902f536c","added_by":"auto","created_at":"2025-11-21 09:59:29","extension":"jpg","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":515426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S6: ACAT1 inhibitor does not alter triglyceride levels in MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-C\u003c/strong\u003e): Total glycerol, Free Glycerol, and Triacylglycerol levels in cell lysates of scramble versus \u003cem\u003eTARDBP\u003c/em\u003eknockdown (TDP-43 KD) treated with and without ACAT1 inhibitor, measured using Promega TriGlo Assay. \u003cstrong\u003eD\u003c/strong\u003e) Total glycerol measured in supernatants of scramble versus TDP-43 knockdown MDMi treated with and without ACAT1 inhibitor (Free glycerol levels were too low to quantify) (N=12). Statistical analysis: 2-Way ANOVA. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental6ACATTGassay.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/3a70cf6f13adfaa337b1ab3e.jpg"},{"id":96452981,"identity":"c9e0c058-ed9a-4133-8086-2180502f56c4","added_by":"auto","created_at":"2025-11-21 09:56:53","extension":"jpg","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":190158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S7: qPCR validation of expression of triglyceride-associated genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-D\u003c/strong\u003e) Gene expression measured using TaqMan qPCR in scramble versus \u003cem\u003eTARDBP\u003c/em\u003e knockdown (TDP-43 KD) MDMi (N=15). Statistical Analysis: Paired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental7TGgeneexpressionandTDPexpressionwithDGATACAT.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/04ed69a9e925b4c0933b2835.jpg"},{"id":96452918,"identity":"ed646ef6-7c2b-48ee-95c9-222f44ab2493","added_by":"auto","created_at":"2025-11-21 09:53:58","extension":"jpg","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":929912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S8: DGAT inhibitor significantly alters gene expression in control and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown MDMi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmaps showing Fluidigm data for gene expression alterations in control vs \u003cem\u003eTARDBP\u003c/em\u003e knockdown (KD), along with effect of DGAT inhibitor on control and \u003cem\u003eTARDBP\u003c/em\u003e knockdown. Fold change is expressed as Log2FC. Genes that were significantly altered (p-value\u0026lt;0.05) are indicated by the black dot and grey dot indicates genes that were close to significance. Statistical analysis: Multiple paired t-test. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental8HeatmapswithDGAT.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/3c28712ab420ee3260fdaf93.jpg"},{"id":96453927,"identity":"d5b17d37-cfa2-4629-8f77-847afd31f602","added_by":"auto","created_at":"2025-11-21 10:02:03","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":23408,"visible":true,"origin":"","legend":"","description":"","filename":"FigS8TDPKDwithDGATinhfluidigmheatmappapersubmission.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/fa210b4627e1d42af022c075.xlsx"},{"id":96452969,"identity":"508d7882-ab62-4694-88b2-d4e803802c79","added_by":"auto","created_at":"2025-11-21 09:56:31","extension":"jpg","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":232791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S9: Fatty acid uptake is altered by CD36 inhibition, but lipid droplets are not\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) C12-BODIPY mean intensity measured by CellProfiler using confocal images for scramble and TDP-43 KD MDMi treated with DGAT inhibitors overnight (N=4). \u003cstrong\u003eB\u003c/strong\u003e) C12-BODIPY mean intensity measured by CellProfiler using confocal images, for scramble and TDP-43 KD MDMi treated with SSO for 4 hours (N=7). \u003cstrong\u003eC\u003c/strong\u003e) LipidTox mean intensity measured by CellProfiler using confocal images for scramble and TDP-43 KD cells +/- SSO for 24 hours (N=5). \u003cstrong\u003eD\u003c/strong\u003e) LipidTox mean intensity for scramble vs KD treated with SSO for 24 hours (N=8). Statistical Analysis: 2 Way ANOVA. Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental9SSOdata.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/6416097c80b1894f41c70c96.jpg"},{"id":96452926,"identity":"91862a21-c1c6-4a98-a005-b0c6fa5462a7","added_by":"auto","created_at":"2025-11-21 09:54:48","extension":"jpg","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":1299899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S10: Lipidomic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-E\u003c/strong\u003e) Heat maps showing lipidomic analysis of certain species: A) Acylcarnitines B) Triglycerides C) Monohexylceramides D) Bis(monoacylglycero)phosphate, and E) Lactosylceramides. Bold species were significant (paired t-test, p\u0026lt;0.05, N=4). Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental10LipidomicsExtended.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/1f7f1b97ca5a970759380e1a.jpg"},{"id":96377312,"identity":"354c17dd-65b7-495f-8f0c-e39267980e4e","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":779057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S11: ALS patient-derived MDMi demographics and mean lipid droplet quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Demographics of TDP-ALS mutant patients and age and sex matched control samples used to obtain PBMCs for MDMi. \u003cstrong\u003eB\u003c/strong\u003e) TARDBP gene expression measured by TaqMan qPCR in control vs TDP-ALS mutant patients (N=3). \u003cstrong\u003eC\u003c/strong\u003e) Confocal images showing LipidTox staining in controls and TDP-ALS mutant MDMi with DGAT inhibitor treatment. \u003cstrong\u003eD\u003c/strong\u003e) Quantification of mean LipidTox intensity using imaging data from CellProfiler in control and TDP-ALS MDMi +/- DGAT inhibitors. \u003cstrong\u003eE\u003c/strong\u003e) Quantification of mean lipid droplet (LD) area by CellProfiler in scramble and TDP-ALS MDMi +/- DGAT inhibitors. \u003cstrong\u003eF\u003c/strong\u003e) Quantification of mean lipid droplet (LD) radius using CellProfiler in scramble and TDP-ALS MDMi +/- DGAT inhibitors. \u003cstrong\u003eG\u003c/strong\u003e) Confocal Images showing LipidTox staining in control vs sALS patient MDMi. \u003cstrong\u003eH\u003c/strong\u003e) LipidTox mean intensity in control and sALS MDMi quantified using CellProfiler (N=4). \u003cstrong\u003eI\u003c/strong\u003e) Mean lipid droplet (LD) area in control and sALS MDMi quantified using CellProfiler (N=3). Statistical analysis D-F) 2-Way ANOVA, B, H, I) Unpaired t-test. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Supplemental11TDPALSPatientMDMiLipidTox.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/f2e655696a18a3af3474e12f.jpg"},{"id":96377314,"identity":"e4e3bf33-e495-4a1d-866e-4d9198fd481e","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"jpg","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":535770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig S12: TDP-ALS samples cluster together whereas sporadic ALS and controls are heterogenous.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Clustered heat map using z-score matrix and row clustering for controls, TDP-ALS and sALS samples. \u003cstrong\u003eB\u003c/strong\u003e) PCA plot for ALS-TDP and matched controls (N=3), and \u003cstrong\u003eC\u003c/strong\u003e) PCA plot for sALS and matched controls (N=6). \u003cstrong\u003eD-E\u003c/strong\u003e) Volcano plots showing Log2FC versus -Log10P for Fluidigm gene expression data in ALS-TDP MDMi compared to controls and sALS MDMi compared to controls. Log2FC calculated using average expression values for each group. Statistical test: unpaired multiple t-test (unadjusted p-values). Data represent the mean ± SEM; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Supplemental12PCAplotsandclusteredheatmapofALSpatientFluidigm.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/29f7f74c05ddc58fac669cf2.jpg"},{"id":96453722,"identity":"fd495ab3-e462-487c-b7ff-137f80d0b0c5","added_by":"auto","created_at":"2025-11-21 10:01:24","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":12553,"visible":true,"origin":"","legend":"","description":"","filename":"FigS12ALSfluidigmvolcanoplotTDPmutantpapersubmission.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/8b7bfcf97b24133e8950d10e.xlsx"},{"id":96453210,"identity":"e0c8f5d2-2e8d-4e3d-b172-c766501862dd","added_by":"auto","created_at":"2025-11-21 09:58:47","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":12576,"visible":true,"origin":"","legend":"","description":"","filename":"FigS12sALSfluidigmvolcanoplotpapersubmission.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/e08ed93d38f42577207b7a5a.xlsx"},{"id":96453209,"identity":"a9ac9955-819f-4bd5-8264-02e2bfa358c0","added_by":"auto","created_at":"2025-11-21 09:58:47","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":604815,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/802c73f6c90689932d4e03ad.docx"},{"id":96377316,"identity":"7b89373b-d4fb-41ee-84ae-28a8e6cef2bf","added_by":"auto","created_at":"2025-11-20 11:33:29","extension":"xlsx","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":12951,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2VolcanoPlotScrvsTDP43KD101025papersubmission.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/56176c86345b832cbaff64ac.xlsx"},{"id":96377321,"identity":"280169f0-f849-44f3-9e00-c1d00be7078a","added_by":"auto","created_at":"2025-11-20 11:33:30","extension":"xlsx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":171975,"visible":true,"origin":"","legend":"","description":"","filename":"Fig9TDP43KDLipidomicsdatapapersubmission.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/cd961a289975d729e0752d44.xlsx"},{"id":96377318,"identity":"1ca54b2c-5592-4403-8548-2738505b8287","added_by":"auto","created_at":"2025-11-20 11:33:30","extension":"xlsx","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":22665,"visible":true,"origin":"","legend":"","description":"","filename":"Fig11ALSpatientMDMiheatmapsfinal052925papersubmission.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8036170/v1/6866be4c8ee8f7280b9c6145.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Loss of Nuclear TDP-43 Impairs Lipid Metabolism in Microglia-Like Cells","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; TDP-43 nuclear depletion causes increased LD, driven by triglyceride accumulation.\u003c/p\u003e\u003cp\u003e\u0026bull; TDP-43 nuclear depletion causes increased phagocytosis and pro-inflammatory cytokine expression.\u003c/p\u003e\u003cp\u003e\u0026bull; Inhibiting triglyceride synthesis using DGAT inhibitors rescues LD and pro-inflammatory phenotype in TDP-43 depleted MDMi\u003c/p\u003e\u003cp\u003e\u0026bull; ALS patient-derived MDMi display increased LD and expression, rescued by DGAT inhibitors\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAmyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder primarily affecting motor neurons. Although only about 10% of cases are familial, an increasing number of genes with diverse biological functions have been linked to both familial and sporadic forms of ALS [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Multiple cellular pathways, including protein degradation, mitochondrial dysfunction, and autophagy, to name a few, have been implicated in disease pathogenesis, yet the precise mechanisms remain poorly understood. Despite this genetic and mechanistic heterogeneity, common features such as metabolic dysfunction, neuroinflammation, and microglial dysregulation consistently emerge across ALS cases [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMicroglia are key innate immune cells in the central nervous system (CNS), known to be dysregulated in various neurodegenerative diseases, including ALS [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although the presence of neuroinflammation, including activation of glial cells and infiltration of peripherally-derived innate and adaptive immune cells, is a consistent hallmark of ALS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the specific mechanism by which microglia contribute to disease pathology is unclear [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Microglia sequencing studies in ALS have demonstrated significant, distinct alterations in gene expression compared to both healthy subjects and individuals with other neurodegenerative diseases [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Transcriptomic studies of post-mortem spinal cord tissues implicate glial activation in ALS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In particular, chronic or excessive microglial activation has been proposed as a mechanism driving the transition from neuroprotective to neurotoxic phenotypes, making microglia a potential therapeutic target in ALS [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAn important characteristic of microglia is that they are highly mobile and dynamic, constantly modifying their shape and membrane structure to survey the environment and respond to pathogens and injury [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Lipids have a crucial role in regulating membrane structure and are therefore important in microglia function [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, lipid microdomains are required for the localization of signaling receptors and regulating immune pathways [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Microglia are also known to be metabolically altered in many neurodegenerative diseases. Notably, lipid droplets (LDs), which store cholesterol esters, triglycerides, and other lipids within cells [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], have been shown to accumulate in aging as well as Alzheimer\u0026rsquo;s disease (AD) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Interestingly, the ApoE4 genotype, which confers significant risk for AD, has been linked to lipid droplet formation in induced pluripotent stem cell (iPSC)-derived microglia. These \u0026lsquo;lipid-laden\u0026rsquo; microglia were enriched in cellular senescence genes and shown to drive neurotoxicity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is also known that lipopolysaccharide (LPS) stimulation in microglia causes accumulation of LDs, which activate inflammatory pathways in the brain that can become neurotoxic over time and contribute to neuroinflammation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The mechanisms underlying microglial LD formation in different contexts remain elusive. Studies have shown metabolic dysregulation, specifically lipid alterations, to be a consistent feature in ALS, with numerous studies demonstrating alterations in lipid pathways in both patient biofluids and disease models [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, the mechanisms regulating lipid dysfunction in ALS, specifically in microglia, have not been well-studied [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e, which encodes the TAR DNA-binding protein 43 (TDP-43), is a key gene implicated in ALS pathology [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. TDP-43 aggregation and nuclear depletion are hallmarks in ~\u0026thinsp;95% of ALS cases [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Previous studies in oligodendrocytes, motor neurons, as well as HeLa and HEK293 cell lines have demonstrated the role of TDP-43 in regulating lipid metabolism, including cholesterol biosynthesis and efflux pathways, but the mechanisms by which these disruptions impact ALS remain unclear. A recent study showed that overexpression of \u003cem\u003eTARDBP\u003c/em\u003e in HEK293 cells resulted in defects in cholesterol biosynthesis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], whereas another study showed that conditional knockout of \u003cem\u003eTARDBP\u003c/em\u003e in mouse oligodendrocytes suppressed cholesterol biosynthesis gene expression [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although these studies implicate a role for TDP-43 in regulating cholesterol biosynthesis via SREBP2, the underlying mechanisms remain to be established, as does the impact on immune cells like microglia, and the contribution to ALS pathology.\u003c/p\u003e\u003cp\u003eHere, we investigated the impact of \u003cem\u003eTARDBP\u003c/em\u003e knockdown on lipid metabolism and microglial function using human MDMi. Previous work has demonstrated that MDMi generated from ALS patient-derived blood display nuclear loss of TDP-43 and exhibit other ALS phenotypes such as altered cytokine expression and function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. We therefore used a \u003cem\u003eTARDBP\u003c/em\u003e shRNA-mediated knockdown model to specifically investigate lipid alterations and determine how loss of TDP-43 impacts microglia function. Additionally, we analyzed patient-derived MDMi from both sporadic ALS (sALS) cases and patients with \u003cem\u003eTARDBP\u003c/em\u003e mutations (TDP-ALS) and observed both shared and distinct features when compared to the shRNA knockdown model.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eTARDBP knockdown causes nuclear TDP-43 depletion in MDMi\u003c/h2\u003e\u003cp\u003eTo investigate how TDP-43 depletion alters lipids in a microglia model, we employed shRNA-mediated gene knockdown in MDMi, achieving an average \u003cem\u003eTARDBP\u003c/em\u003e knockdown efficiency of approximately 70% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The demographic information of donors used can be found in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Knockdown efficiency was validated by both immunocytochemistry (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and Western blot analysis (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, B\u003c/b\u003e). Confocal imaging showed a 40% reduction in mean nuclear TDP-43 intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and a significant reduction in the nuclear to cytoplasmic ratio of TDP-43 staining (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/b\u003e), but no significant change in cytoplasmic TDP-43 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). We also stained for phospho-TDP-43 (pTDP-43), given that pTDP-43 is known to increase in ALS and is a marker for cytosolic TDP-43 accumulation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, there were no significant alterations in nuclear or cytoplasmic pTDP-43 (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e A-C\u003c/b\u003e), or the nuclear to cytoplasmic ratio of pTDP-43 (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTARDBP knockdown significantly alters lipid metabolism and immune genes in MDMi.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the impact of \u003cem\u003eTARDBP\u003c/em\u003e knockdown (TDP-43 KD), we used the Fluidigm Biomark microfluidics system to analyze gene expression with a targeted panel of 110 genes related to lipid metabolism (cholesterol, phospholipid/sphingolipid, triglyceride, and fatty acid metabolism), glycolysis, and immune function, and compared results to scramble controls. STRING analysis[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] shows the genes in three main clusters as expected - immune function, lipid metabolism, and glycolysis, although many of the genes have related functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). As demonstrated in the volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), several genes were significantly altered in the knockdown. Interestingly, of the 110 genes measured, only 27 were upregulated. 14 of these reached statistical significance with an unadjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, while only 2 were significant with an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Many of these genes were related to immune function, with pro-inflammatory cytokines \u003cem\u003eCCL4\u003c/em\u003e and \u003cem\u003eIL1β\u003c/em\u003e being most highly upregulated \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). \u003cem\u003eTREM1\u003c/em\u003e, associated with increased inflammatory phenotypes, was also highly upregulated. Among the lipid cluster, fatty acid-associated genes like \u003cem\u003eFABP4\u003c/em\u003e, \u003cem\u003eELOVL3\u003c/em\u003e, and \u003cem\u003eBSCL2\u003c/em\u003e had the greatest fold-change increase in the knockdown compared to scramble control. These genes are involved in fatty acid uptake and transport, elongation, and lipid droplet formation [\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOf the 110 analyzed genes, 84 were downregulated, with 59 showing statistically significant changes with an unadjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, while 28 of those were significant with an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Among the top 15 downregulated genes, \u003cem\u003eINSIG1\u003c/em\u003e, which encodes the protein INSIG1 that binds to and regulates SREBP2, exhibited the greatest reduction in expression (Log2FC = -2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This is consistent with a previous study in mouse oligodendrocytes where conditional knockout of \u003cem\u003eTARDBP\u003c/em\u003e resulted in a significant reduction in \u003cem\u003eINSIG1\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Other genes related to \u003cem\u003eINSIG1\u003c/em\u003e, such as \u003cem\u003eSREBP2\u003c/em\u003e and \u003cem\u003eMBTPS1\u003c/em\u003e, were also downregulated in our study, suggesting suppression of cholesterol biosynthesis pathways, again consistent with previous studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Other significantly downregulated genes included \u003cem\u003eTREM2\u003c/em\u003e and \u003cem\u003eLPL\u003c/em\u003e, both highly expressed in microglia, and known to be involved in neurodegenerative diseases like AD through their function in regulating microglia lipid homeostasis as well as immune function [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. \u003cem\u003eTREM2\u003c/em\u003e expression has been found to be increased in spinal cords of SOD1-ALS mice and in reactive microglia from ALS postmortem tissues [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The Alzheimer's disease-associated genetic variant TREM2 R47H, which modulates ligand binding, has also been implicated in ALS, supporting the importance of correctly functioning lipid metabolism in microglia [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe confirmed the increase in \u003cem\u003eIL1β\u003c/em\u003e expression by qPCR and ELISA and found that it was indeed significantly upregulated at both the gene and protein level (\u003cb\u003eFig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA, B\u003c/b\u003e). We also measured soluble TREM2 (a biologically active fragment of TREM2) and found a significant reduction in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown (\u003cb\u003eFig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC\u003c/b\u003e), suggesting a possible downregulation of anti-inflammatory pathways while pro-inflammatory pathways are upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eInterestingly, genes related to glycolysis (\u003cem\u003ePFKP\u003c/em\u003e, \u003cem\u003eG6PD\u003c/em\u003e, and \u003cem\u003eGLUT1)\u003c/em\u003e were among those most significantly downregulated in the knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), although hexokinase 1 (\u003cem\u003eHK1\u003c/em\u003e) which phosphorylates glucose to G-6-P was significantly upregulated, suggesting alterations in glucose metabolism that could contribute to bioenergetic dysregulation, which is a known feature of ALS [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTDP-43 depletion causes increased lipid droplet accumulation in MDMi.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLD accumulation has emerged as a significant phenotype in diseased/abnormal microglia and has been associated with AD, tauopathies, and aging [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], although the pathomechanism is not clear. Given the role of LD accumulation as a marker for microglia dysfunction, as well as significant alterations of genes like \u003cem\u003eTREM2, LPL, BSCL2\u003c/em\u003e, and \u003cem\u003eFABP4\u003c/em\u003e in our knockdown, we sought to determine whether TDP-43-depleted MDMi have altered LD accumulation. Using LipidTox green to stain for neutral lipids (usually stored in lipid droplets), we found a significant increase in LD intensity in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown cells compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B), pointing to altered lipid metabolism and bioenergetics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTDP-43 depletion does not alter cholesterol uptake in MDMi.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we investigated whether the accumulation of LDs was driven by changes in cholesterol uptake or efflux, given the dramatic alterations we observed in the expression of cholesterol-associated genes upon \u003cem\u003eTARDBP\u003c/em\u003e knockdown.\u003c/p\u003e\u003cp\u003eUnder conditions of low intracellular cholesterol levels, INSIG1 dissociates from the SCAP-SREBP2 complex, allowing SREBP2 to translocate to the endoplasmic reticulum (ER) from the Golgi and activate the transcription of various cholesterol biosynthesis genes, including HMGCR [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Downregulation of these genes, as observed in our \u003cem\u003eTARDBP\u003c/em\u003e knockdown, may impair the cell\u0026rsquo;s capacity to activate cholesterol biosynthesis and could increase external uptake of cholesterol. Using a fluorescently labeled analogue of cholesterol (NBD-cholesterol), which has been previously shown to mimic cholesterol uptake via lipoproteins in cells [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], we measured the uptake of cholesterol in MDMi. Interestingly, we found no difference in NBD-cholesterol uptake between controls and \u003cem\u003eTARDBP\u003c/em\u003e knockdown cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). To verify that this assay does in fact recapitulate how cells physiologically uptake cholesterol from the media, we repeated the experiment in the presence of LPS stimulation, as other studies have shown that LPS treatment induces cholesterol uptake in immune cells [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Indeed, we saw that with LPS stimulation, both scramble control and \u003cem\u003eTARDBP\u003c/em\u003e knockdown cells exhibit an increase in NBD-cholesterol uptake; however, this increase was only significant in scramble controls (\u003cb\u003eFig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA, B\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eWhen comparing the ratio of NBD-cholesterol uptake in LPS-stimulated to unstimulated conditions, we observed significantly lower uptake in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown MDMi compared to the scramble control, indicating a reduced response to LPS (\u003cb\u003eFig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC\u003c/b\u003e). These findings suggest that although \u003cem\u003eINSIG1 and SREBP2\u003c/em\u003e expression are decreased and cholesterol synthesis may be depressed, TDP-43 depleted MDMi do not increase cholesterol uptake. Additionally, when stimulated with LPS, TDP-43 depleted MDMi do not have a significant induction of cholesterol uptake as seen in the control. Gene expression of \u003cem\u003eSCARB1\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, and \u003cem\u003eLRP1\u003c/em\u003e, receptors involved in lipoprotein uptake, was also downregulated in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown, which could explain why these cells are unable to upregulate cholesterol uptake. Taken together, these data suggest that the lipid droplet accumulation observed in our \u003cem\u003eTARDBP\u003c/em\u003e knockdown is not driven by cholesterol uptake.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTARDBP knockdown in MDMi reduces cellular total and free cholesterol levels.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur gene expression analysis also revealed downregulation of \u003cem\u003eABCA1\u003c/em\u003e and \u003cem\u003eABCC4\u003c/em\u003e, suggesting possible impairment of cholesterol efflux. Indeed, previous reports have shown that \u003cem\u003eABCA1\u003c/em\u003e deletion causes an accumulation of cholesterol esters as the cell is unable to efflux excess cholesterol [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. To determine whether the lipid droplet accumulation observed in our \u003cem\u003eTARDBP\u003c/em\u003e knockdown MDMi is a result of cholesterol ester accumulation, we measured total cholesterol (TC), free cholesterol (FC), and cholesterol esters (CE) in cells and supernatants of scramble and \u003cem\u003eTARDBP\u003c/em\u003e knockdown MDMi. Cells are known to uptake free cholesterol from lipoproteins, which are esterified by the enzyme Acyl-coenzyme A: cholesterol acyltransferase-1 (ACAT1) to cholesterol esters, for storage in LDs.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e knockdown cells exhibited decreased TC and FC, with no significant change in CEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). Supernatants showed a similar trend, with TC and FC levels being significantly reduced while CE was unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-F). The overall reduction in TC and FC suggests reduced cholesterol synthesis in \u003cem\u003eTARDBP\u003c/em\u003e knockdown cells, with a concomitant decrease in efflux. There was no significant difference in the ratio of FC to TC in scramble compared to \u003cem\u003eTARDBP\u003c/em\u003e knockdown in cells and supernatants, which supports the idea that cholesterol esterification does not drive LD accumulation in these cells (\u003cb\u003eFig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eD-E\u003c/b\u003e), as increased esterification would reduce the proportion of FC. This is also supported by the NBD-cholesterol uptake data, which showed no significant changes in uptake between scramble and knockdown.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further confirm that the lipid droplet phenotype observed in \u003cem\u003eTARDBP\u003c/em\u003e knockdown cells is not a result of cholesterol ester accumulation, we treated the scramble and knockdown MDMi with an ACAT1 inhibitor, which has previously been shown to reduce free cholesterol and cholesterol esters in cells [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. As expected, ACAT1 inhibition led to a significant reduction in lipid droplets in scramble control cells; however, no such reduction was observed in TDP-43\u0026ndash;depleted MDMi (\u003cb\u003eFig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eF\u003c/b\u003e), supporting the above finding that cholesterol esterification does not contribute to LD accumulation in these cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTDP-43 depleted MDMi exhibit increased glycerol and triglyceride levels.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLDs are known to be comprised of both cholesterol esters and triglycerides [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. We therefore measured triglyceride levels in these cells to interrogate whether triglyceride, rather than cholesterol ester accumulation, could be contributing to the LD phenotype observed in \u003cem\u003eTARDBP\u003c/em\u003e knockdown cells. Triglyceride (TG) levels are calculated by subtracting the difference between total glycerol and free glycerol (which can be used to synthesize TGs). Interestingly, TGs were significantly increased in \u003cem\u003eTARDBP\u003c/em\u003e knockdown cells, as were total glycerol levels, whereas free glycerol was not significantly altered (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNo significant alterations were observed in the supernatants (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F). However, the cell-to-supernatant ratio of total glycerol and TGs was significantly increased in the knockdown (\u003cb\u003eFig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA-C\u003c/b\u003e), while free glycerol remained unchanged. The increase in TGs and total glycerol, with stable free glycerol, suggests enhanced TG synthesis or possible defects in lipolysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDGAT1/2 inhibitors reduce lipid droplets in TARDBP knockdown MDMi.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMicroglia have been shown to contain higher levels of TGs compared to other brain cell types, specifically astrocytes and neurons [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Additionally, a recent study found that triglyceride metabolism may be key in regulating microglia inflammatory pathways [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In this study, inhibitors of DGAT 1 and 2 (diacylglycerol acyltransferases 1 and 2), which control the rate-limiting step of triglyceride biosynthesis from diacylglycerol and fatty acids, were used to reduce triglyceride accumulation in iPSC-derived microglia. Given the possibility of increased TG synthesis in our \u003cem\u003eTARDBP\u003c/em\u003e knockdown model, we used a similar approach to determine whether DGAT inhibitors could reverse the observed LD phenotype.\u003c/p\u003e\u003cp\u003eFirst, we measured the effect of the DGAT inhibitors on glycerol and TG levels. Unsurprisingly, we found that DGAT inhibitors reduced total glycerol, free glycerol, and TGs in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). However, no significant changes were seen in the scramble control, suggesting that TG accumulation, and possibly an increase in the activity of DGAT 1 and 2 enzymes, only occurs when nuclear TDP-43 levels are reduced. Interestingly, total glycerol levels were increased in the supernatants of DGAT inhibitor-treated samples, both in scramble and \u003cem\u003eTARDBP\u003c/em\u003e knockdown conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), suggesting that inhibiting triglyceride synthesis either reduces the incorporation of glycerol from the media, or increases the secretion of excess glycerol. As an additional control, we measured glycerol and TG levels in cells treated with ACAT1 inhibitors. As expected, ACAT1 inhibition did not alter total glycerol or TG levels in the scramble or knockdown cells, and there was no change in total glycerol levels in the supernatants (\u003cb\u003eFig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA-D\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe then measured LD accumulation in the DGAT inhibitor-treated cells and found a significant decrease in the fluorescence intensity of LipidTox in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown in treated compared with untreated cells; this effect was not seen in the scramble controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Taken together, these data suggest that increased lipid droplet formation in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown is a result of TG accumulation, possibly due to increased synthesis driven by DGAT1 and 2.\u003c/p\u003e\u003cp\u003eTo further interrogate whether \u003cem\u003eTARBDP\u003c/em\u003e knockdown MDMi are in fact accumulating triglycerides via upregulated synthesis, we examined the gene expression of \u003cem\u003eDGAT1\u003c/em\u003e and \u003cem\u003eDGAT2\u003c/em\u003e in the knockdown and found, unexpectedly, that their expression was decreased \u003cb\u003e(Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eA, B\u003c/b\u003e). However, triglyceride hydrolysis genes \u003cem\u003eATGL\u003c/em\u003e and \u003cem\u003eHSL\u003c/em\u003e were increased (\u003cb\u003eFig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eC, D\u003c/b\u003e). This suggests that although TDP-43-depleted MDMi may not be increasing \u003cem\u003ede novo\u003c/em\u003e triglyceride synthesis, triglyceride hydrolysis could be upregulated due to increased storage and accumulation. It is also possible that \u003cem\u003eDGAT1\u003c/em\u003e and \u003cem\u003eDGAT2\u003c/em\u003e expression is reduced as a negative feedback mechanism to counter increased enzyme activity. Additionally, DGAT2 is normally localized in the ER, but it is also found within LD membranes and facilitates their expansion at the ER-LD interface. DGAT1, on the other hand, converts exogenous pre-formed fatty acids into triglycerides [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. DGAT1 and 2 could therefore be involved in lipid droplet expansion and regulation of exogenous fatty acid uptake, thus resulting in decreased lipid droplet accumulation when they are inhibited.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTDP-43 depleted MDMi have increased fatty acid uptake.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe then wanted to examine whether \u003cem\u003eTARDBP\u003c/em\u003e knockdown MDMi exhibit alterations in fatty acid uptake, which might explain the increase in TG synthesis or storage. \u003cem\u003eFABP4\u003c/em\u003e, involved in regulating fatty acid uptake and transport, and \u003cem\u003eELOVL3\u003c/em\u003e, involved in fatty acid elongation, were among the most upregulated metabolism genes in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown. Additionally, \u003cem\u003eACSL4\u003c/em\u003e and \u003cem\u003eACSL1\u003c/em\u003e (Acyl-CoA Synthetase Long-Chain family), which encode enzymes that activate long-chain fatty acids by converting them into fatty acyl-CoA, were also upregulated. Thus, we hypothesized that TDP-43 depletion upregulates fatty acid uptake, which could contribute to increased TG storage and LD accumulation. Indeed, the uptake of BODIPY-labeled C12 fatty acid was significantly increased in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown condition, as shown by quantification of confocal imaging (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTDP-43 depleted MDMi have altered morphology and function.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFinally, in addition to the effects on lipid metabolism, we wanted to understand the effect of \u003cem\u003eTARDBP\u003c/em\u003e knockdown on conventional microglia functions. Microglia are known to change their morphology depending on their activation state and function. Surveilling and homeostatic microglia are thought to be ramified, whereas activated or phagocytic microglia have an ameboid morphology [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Here, we found that TDP-43-depleted MDMi had a more rounded morphology and a smaller cell body (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). This was quantified using the \u0026ldquo;compactness\u0026rdquo; metric in CellProfiler, where lower values indicate a more rounded morphology (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Rounded microglia have been associated with enhanced phagocytosis [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], consistent with previous reports showing that microglia in ALS exhibit a more activated phenotype [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. In agreement with this, we found that TDP-43 depleted cells exhibited an increase in dextran uptake, suggesting greater phagocytic activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhagocytosis and IL1β levels are reduced in TDP-43 depleted MDMi treated with DGAT1 and DGAT2 inhibitors.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo confirm that the inflammatory and phagocytic phenotypes observed in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown microglia are driven by elevated TG levels, we measured these outcomes in MDMi treated with DGAT inhibitors. We found no changes in cell morphology (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). However, dextran uptake was significantly reduced in DGAT inhibitor-treated MDMi (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF), and \u003cem\u003eIL1β\u003c/em\u003e expression was significantly reduced in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown but not in the scramble (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG), which is consistent with the effect of DGAT inhibitors on LD and TG accumulation. We also found that ACAT1 inhibition did not alter \u003cem\u003eIL1β\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH), indicating that the \u003cem\u003eIL1β\u003c/em\u003e gene expression increase is downstream of TG accumulation. The fact that morphology was not altered upon DGAT inhibition suggests that it may be a result of alterations in cytoskeletal proteins, which are known to be transcriptionally regulated by TDP-43 [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo understand how inhibiting TG synthesis affects the pathways that were altered by \u003cem\u003eTADRBP\u003c/em\u003e knockdown, we analyzed gene expression changes in DGAT inhibitor-treated samples with Fluidigm microfluidic qPCR. Interestingly, we found that most of the cholesterol biosynthesis genes remain downregulated. However, key fatty acid genes and LD-associated genes like \u003cem\u003eFABP4\u003c/em\u003e, \u003cem\u003eELOVL3\u003c/em\u003e, and \u003cem\u003eBSCL2\u003c/em\u003e, which were upregulated in the knockdown, are reduced with DGAT inhibition. Many upregulated immune genes are also reduced significantly (\u003cb\u003eFig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e\u003c/b\u003e). To determine whether DGAT inhibition reduces fatty acid uptake, we measured BODIPY-C12 uptake and found a significant reduction in the knockdown, but not in the scramble (\u003cb\u003eFig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eA\u003c/b\u003e). Fatty acid uptake can also be reduced by directly inhibiting a fatty acid receptor such as CD36 (\u003cb\u003eFig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eB\u003c/b\u003e); however, we found that direct inhibition of CD36 (with both 4-hour and 24-hour SSO treatment) did not reduce LD intensity (\u003cb\u003eFig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eC, D\u003c/b\u003e). In fact, LD intensity was increased in control cells with 4-hour SSO treatment (\u003cb\u003eFig. \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eC\u003c/b\u003e), suggesting that there may be compensatory effects of inhibiting CD36-mediated fatty acid uptake, or that TDP-43 mediated increases in fatty acid uptake are regulated through more complex mechanisms.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLipidomic analysis of TARDBP knockdown MDMi demonstrates increases in monounsaturated triglycerides.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo better understand the lipid profile in the TDP-43 depleted MDMi, we ran targeted LCMS-based lipidomic analysis and found alterations in many lipid species in the knockdown compared to the scramble control. Significant alterations were found in acylcarnitine (AC), triglyceride (TG), diacylglyceride (DG), and monoacylglyceride (MG) species (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-D). Notable trends in TG alterations included an increase in various mono and poly-unsaturated species of TGs (18:1 and 20:4), which are usually stored in neutral lipid droplets, while saturated TGs (18:0) were reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE), with TG 54:0/18:0 being significantly reduced in the knockdown (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We also found alterations in Bis(monoacylglycero)phosphate (BMP), unique phospholipids found in the inner membranes of late endosomes and lysosomes that play an important role in lysosomal stability and lipid degradation (\u003cb\u003eFig. \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e\u003c/b\u003e). Most species of mono-hexosyl ceramides (glucosylceramide), which are hydrolyzed in the lysosome to provide the cell with glucose and ceramide, were decreased, supporting the alterations in BMP that suggest defects in lysosomal degradation. Long-chain ACs (AC C12:0 and C18:0) were decreased while short-chain ACs (AC C3:0) were increased, which could indicate increased oxidation of fatty acids (\u003cb\u003eFig. \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e\u003c/b\u003e). Taken together, the lipidomics data suggests alterations in triglyceride and fatty acid metabolism, as well as possible dysfunction in lysosomal degradation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eALS-patient derived MDMi show increased lipid droplets and IL1β, rescued by DGAT inhibition.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe obtained peripheral blood mononuclear cells (PBMCs) from three individuals with mutations in \u003cem\u003eTARDBP\u003c/em\u003e, one of whom was diagnosed with ALS and two of whom are pre-symptomatic mutation carriers, to make MDMi from (\u003cb\u003eFig. \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003eA\u003c/b\u003e). All three patients had missense mutations that have been shown to cause TDP-43 loss of function, mislocalisation, and aggregation [\u003cspan additionalcitationids=\"CR73 CR74 CR75\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], although \u003cem\u003eTARDBP\u003c/em\u003e expression as measured by qPCR was unchanged (\u003cb\u003eFig. \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003eB\u003c/b\u003e). Compared to age and sex-matched controls, we found a significant increase in both \u003cem\u003eIL1β\u003c/em\u003e gene expression and protein levels in the supernatants of the \u003cem\u003eTARDBP\u003c/em\u003e-mutant (TDP-ALS) MDMi, which were reduced with DGAT inhibitor treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, B). We also obtained PBMCs from patients with sporadic ALS (sALS) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e) and observed the same increase in \u003cem\u003eIL1β\u003c/em\u003e expression and protein levels in their MDMi (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC-D). LD intensity was increased in the TDP-ALS MDMi, but this only reached significance when including every image in the dataset rather than the mean of all images for each sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE \u003cb\u003eand Fig. \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003eD\u003c/b\u003e) and was rescued with DGAT inhibitor treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE \u003cb\u003eand Fig. \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003eC-D\u003c/b\u003e). We also measured mean LD area and found that this was not significantly altered (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF), although the maximum LD radius was significantly higher in the TDP-ALS MDMi compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eG), as seen in confocal images (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eH). DGAT inhibition did not have a significant effect on LD area or maximum radius in TDP-ALS MDMi (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF \u003cb\u003eand Fig. \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003eE-F\u003c/b\u003e), and in fact, DGAT inhibitor treatment seems to slightly increase LD area even though mean intensity was reduced. The sALS MDMi, on the other hand, showed a significant \u003cem\u003edecrease\u003c/em\u003e in mean LipidTox intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eI), whereas LD area and maximum radius were significantly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eJ, K), as also evident in imaging data (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eL). Again, significant trends were seen when every image was included in a nested analysis rather than using the mean of each donor (\u003cb\u003eFig. \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003eH, I\u003c/b\u003e), pointing to large variability between cells. This suggests that, while overall there were fewer LDs in sALS MDMi, resulting in lower mean intensity, there was a greater accumulation of large LDs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, we performed Fluidigm microfluidic qPCR analysis on the patient-derived MDMi, along with healthy control samples and DGAT inhibitor-treated samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Notable differences were observed between TDP-ALS and sALS samples compared to healthy controls (\u003cb\u003eFig. \u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003eS12\u003c/span\u003eA\u003c/b\u003e). Principal component analysis (PCA) revealed a clear separation between TDP-ALS MDMi and their age and sex-matched controls (\u003cb\u003eFig. \u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003eS12\u003c/span\u003eB\u003c/b\u003e). In contrast, the separation between sALS samples and their matched controls was less distinct, suggesting greater heterogeneity or subtler transcriptional changes in the sALS group (\u003cb\u003eFig. \u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003eS12\u003c/span\u003eC\u003c/b\u003e). Volcano plots show gene alterations in both TDP-ALS MDMi, and sALS MDMi compared to matched controls (\u003cb\u003eFig \u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003eS12\u003c/span\u003eD-E\u003c/b\u003e). There were not many genes significantly altered, even with unadjusted p-values, presumably due to the low sample numbers and individual heterogeneity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also found divergent gene expression patterns between the patient-derived MDMi and shRNA knockdown MDMi. The TDP-ALS MDMi showed increased expression of many cholesterol biosynthesis genes (\u003cem\u003eDHCR7\u003c/em\u003e, \u003cem\u003eINSIG1\u003c/em\u003e, \u003cem\u003eSREBP2\u003c/em\u003e), which were reduced with DGAT inhibitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). This contrasts with the knockdown, where \u003cem\u003eSREBP2\u003c/em\u003e and \u003cem\u003eINSIG1\u003c/em\u003e were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Some consistencies with the knockdown were the increased expression of triglyceride and fatty acid metabolism genes in the TDP-ALS MDMi, which are not seen in the sALS MDMi (including \u003cem\u003eFABP4\u003c/em\u003e, \u003cem\u003eFADS1\u003c/em\u003e, \u003cem\u003eMGAT1\u003c/em\u003e, and \u003cem\u003eDGAT2\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). These data suggest that altered fatty acid and triglyceride metabolism may be specific to mutations in \u003cem\u003eTARDBP\u003c/em\u003e, and possibly nuclear depletion of TDP-43, whereas in sALS cases, there may be other factors contributing to disrupted lipid metabolism that cause LD accumulation and altered immune responses.\u003c/p\u003e\u003cp\u003eTaken together, our data demonstrate that TDP-43 plays a critical role in regulating lipid metabolism in microglia. Specifically, nuclear depletion of TDP-43 results in an accumulation of TGs, likely via increased uptake of fatty acids and upregulated TG storage caused by bioenergetic alterations. TDP-43 nuclear depletion also results in increased phagocytic capacity of microglia and higher secretion of pro-inflammatory cytokines like IL1β, which is downstream of TG accumulation. Patients with mutations in \u003cem\u003eTARDBP\u003c/em\u003e exhibit similar phenotypes, which are reversed with DGAT inhibition, suggesting that lipid pathways, specifically TG metabolism may be dysregulated in \u003cem\u003eTARDBP\u003c/em\u003e-driven ALS. MDMi from sALS patients also accumulate large LDs, but show distinct gene expression profiles, suggesting a different mechanism of LD accumulation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough mutations in \u003cem\u003eTARDBP\u003c/em\u003e are rare and make up only 5% of the total familial ALS population, TDP-43 pathology is present in over 90% of ALS patients. Additionally, it is observed in over 50% of Alzheimer's disease (AD) cases and some Parkinson's disease (PD) cases [\u003cspan additionalcitationids=\"CR78 CR79 CR80\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Rather than focusing on specific TARDBP mutations, we employed a general loss-of-function model to investigate the broader cellular consequences of TDP-43 dysfunction, aiming to identify mechanisms relevant across multiple neurodegenerative conditions. Loss-of-function models effectively mimic the pathological effects of TDP-43 aggregation [\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], and in fact, recent studies have shown that inducing TDP-43 aggregation leads to loss of nuclear TDP-43 [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Notably, TDP-43 depletion from microglia has been shown to exacerbate neuroinflammation, pointing to the importance of studying TDP-43 pathology in non-neuronal cells [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, we achieved significant nuclear depletion of TDP-43 using shRNA knockdown in monocyte-derived microglia-like cells (MDMi). Unlike patient-derived MDMi in previous studies, our model did not exhibit cytoplasmic aggregation or phospho-TDP-43, suggesting that nuclear depletion alone is insufficient for these pathological features but still drives significant metabolic and immune alterations. Our data demonstrates a clear link between TDP-43 nuclear depletion and triglyceride (TG) alterations that contribute to lipid droplet (LD) accumulation and altered immune responses in MDMi. While previous studies have demonstrated that microglia LD accumulation can occur via upregulation of TG pathways [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], this has not been shown in the context of ALS. Excessive LD accumulation could be a consequence of several factors, and their varying composition can affect functional outcomes. In a study using both human AD brain tissue and iPSC-derived microglia with different ApoE genotypes (ApoE3/E3 vs ApoE4/E4), microglia with ApoE4/E4 genotype had increased LDs, greater expression of ACSL1 when exposed to fibrillar Aβ and an induction of TG synthesis. These cells were also found to express markers of \u0026ldquo;cellular senescence\u0026rdquo; and exhibit less phagocytosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Here, we found that TG-driven LDs in fact result in increased phagocytic activity, which points to heterogeneity in the effects of LD accumulation.\u003c/p\u003e\u003cp\u003eSeveral genes associated with fatty acid metabolism, including \u003cem\u003eACSL1\u003c/em\u003e, \u003cem\u003eFABP4, ELOVL3, and ACSL4\u003c/em\u003e, were upregulated in our knockdown. In addition to the involvement of \u003cem\u003eACSL1\u003c/em\u003e with LD-accumulating microglia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], some of these genes have also been linked to lipid dysregulation and ALS. IPSC-derived microglia with mutations in profilin-1(PFN1) that are causative for ALS were shown to exhibit upregulated \u003cem\u003eFABP4\u003c/em\u003e and \u003cem\u003eFABP5\u003c/em\u003e [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Other studies have linked \u003cem\u003eFABP4\u003c/em\u003e to increased TG synthesis and LD accumulation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. \u003cem\u003eELOVL3\u003c/em\u003e was found to be significantly increased in \u003cem\u003eLPL\u003c/em\u003e knockdown microglia, which also exhibit increased LD formation, reduced cholesterol synthesis and efflux, and increased inflammation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which is in line with our measured phenotypes. In our knockdown, these genes were downregulated with inhibition of DGAT 1 and 2 enzymes, suggesting that inhibiting triglyceride synthesis triggers a feedback mechanism that downregulates fatty acid uptake/metabolism pathways.\u003c/p\u003e\u003cp\u003eMetabolic alterations and bioenergetic shifts are well-documented in ALS, with many studies reporting a transition from glycolysis to fatty acid oxidation, particularly in glycolytic tissues such as skeletal muscle [\u003cspan additionalcitationids=\"CR91\" citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Recent findings from our group [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e] demonstrated that motor neurons from ALS SOD1 mutant mice exhibit upregulation of glucose, fatty acid, and amino acid catabolism, with impaired oxidative phosphorylation (OXPHOS) and increased fatty acid oxidation. In our current study, we found significant downregulation in the expression of \u003cem\u003ePFKP\u003c/em\u003e and \u003cem\u003eG6PD\u003c/em\u003e, both critical for glycolysis. Prior studies have linked TDP-43 to PFKP regulation, with TDP-43 loss-of-function reducing \u003cem\u003ePFKP\u003c/em\u003e expression and activity, potentially through cryptic exon inclusion [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan additionalcitationids=\"CR95\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Our findings align with these reports, supporting a model in which loss of nuclear TDP-43 leads to impaired glycolysis and a shift toward fatty acid uptake and utilization.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e knockdown resulted in notable changes in immune-related gene expression. \u003cem\u003eTREM2\u003c/em\u003e and \u003cem\u003eIL10\u003c/em\u003e were significantly downregulated, while \u003cem\u003eTREM1\u003c/em\u003e, \u003cem\u003eIL1Β\u003c/em\u003e, and \u003cem\u003eCCL4\u003c/em\u003e were upregulated. The opposing roles of TREM1 and TREM2 in inflammation suggest that loss of nuclear TDP-43 skews microglia toward a pro-inflammatory state [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. Interestingly, \u003cem\u003eTREM2\u003c/em\u003e is upregulated in DAM and microglia associated with neurodegeneration, but not in LDAM (lipid droplet-accumulating microglia), which are thought to be associated with aging and Alzheimer\u0026rsquo;s Disease [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Although sequencing studies have shown an upregulation of TREM2 (along with other DAM genes) in ALS microglia, it is known that TREM2 genetic variation (such as in the R47H variant) is also associated with ALS. Additionally, TREM2 is required for the protective role in attenuating the expression of pro-inflammatory mediators, including iNOS, TNFα, IL-1β, and IL-6, as well as mediating phagocytosis of TDP-43 aggregates in the context of ALS. Our data demonstrating a decrease in soluble TREM2 protein therefore supports defects in immune responses in \u003cem\u003eTARDBP\u003c/em\u003e knockdown MDMi. The observed increase in expression of \u003cem\u003eCCL4\u003c/em\u003e, which encodes a chemokine that correlates positively with better ALS functional scores [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], suggests a possible early-stage protective response that could become detrimental over time. Notably, treatment with DGAT inhibitors reduced \u003cem\u003eIL1Β\u003c/em\u003e, \u003cem\u003eCCL4\u003c/em\u003e, and \u003cem\u003eTREM1\u003c/em\u003e expression, but did not alter \u003cem\u003eTREM2\u003c/em\u003e, linking TG accumulation specifically to pro-inflammatory pathways (21,93). Treatment with DGAT inhibitors also significantly reduced \u003cem\u003eNLRP3\u003c/em\u003e, and \u003cem\u003eIL18\u003c/em\u003e gene expression, and more importantly, IL-1β protein levels, implicating the inflammasome in the inflammatory phenotypes observed in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown.\u003c/p\u003e\u003cp\u003eThe role of LD composition in microglia function remains poorly understood. While TREM2-deficient microglia accumulate CE-rich LDs, which can be rescued by ACAT1 inhibitors [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], our data suggest that TDP-43 deficiency leads to TG-driven LD accumulation, which cannot be rescued by ACAT1 inhibitors. Although we do see a decrease in soluble TREM2 protein levels in our model, our LD phenotype is driven by triglycerides rather than cholesterol esters. Notably, we found that inhibiting ACAT1 reduced LDs in the scramble control, whereas inhibiting DGAT1 and 2 reduced LDs in the knockdown, suggesting that TDP-43 depletion leads to a shift in LD composition in MDMi. Here we observe a distinct phenotype where LD accumulation is accompanied by increased phagocytosis, unlike other studies that have shown impaired phagocytosis in LD-accumulating microglia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. Some studies have also demonstrated the importance of LD accumulation in driving anti-inflammatory responses in microglia [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. This highlights the fact that heterogeneity in LD-associated phenotypes needs further characterization in the context of neurodegenerative diseases involving lipid alterations. In our model, nuclear depletion of TDP-43 could be driving TG-mediated inflammatory pathways, which increase baseline activation of MDMi and increase phagocytic activity.\u003c/p\u003e\u003cp\u003eOur lipidomic analysis supports the observed phenotypes, as we found several unsaturated TGs to be elevated in the \u003cem\u003eTARDBP\u003c/em\u003e knockdown. These were primarily TGs containing oleic acid (18:1), which are known to accumulate in neutral lipid droplets. Interestingly, previous studies have shown accumulation of oleic acid (OA18:1) in a model of Parkinson\u0026rsquo;s Disease [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], and macrophages are known to accumulate unsaturated TGs in the \u0026ldquo;M1 polarization state\u0026rdquo;, associated with inflammation [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. Acyl carnitines (ACs) are fatty acids that are transported to mitochondria for beta-oxidation, and alterations are an indication of mitochondrial dysfunction. The decrease in long-chain ACs (C12:0) and corresponding increase in short-chain ACs (AC 3:0) suggests increased fatty acid oxidation. This could also potentially implicate incomplete fatty acid oxidation as a consequence of excessive fatty acid uptake for ATP production in a state of impaired glycolysis (supported by the downregulation of glycolytic genes in our Fluidigm data). Accumulation of short-chain ACs is also associated with inflammatory responses and oxidative stress in a variety of metabolic contexts [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e], and alterations in short-chain ACs have been found in neurodegeneration and aging [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Finally, we found reductions in BMPs and lactosylceramides, and an increase in mono hexosyl ceramides, both of which are an indication of impairments in lysosomal function and lipophagy [\u003cspan additionalcitationids=\"CR111\" citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. This could imply an alternate mechanism of TG accumulation that results from defects in lysosomal degradation of lipids, rather than increased \u003cem\u003ede novo\u003c/em\u003e synthesis.\u003c/p\u003e\u003cp\u003eOverall, our findings suggest a \u003cem\u003eTARDBP\u003c/em\u003e-specific metabolic shift, characterized by reduced cholesterol biosynthesis and uptake, coupled with a possible impairment in glycolysis, resulting in increased fatty acid uptake and TG accumulation. The observed TG alterations are not unique to our study and have been found in ALS patient serum, Parkinson\u0026rsquo;s Disease iPSC-motor neurons, as well as ApoE4 microglia [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan additionalcitationids=\"CR114 CR115\" citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e], although AD lipidomic studies have found more drastic alterations in phospholipids, sphingolipids, and cholesterol esters [\u003cspan additionalcitationids=\"CR118 CR119\" citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. However as described above, the LD phenotype observed in our TDP-43-depleted MDMi is unique in its effect on microglia function, and this could be explained by bioenergetic alterations that specifically affect glycolysis and fatty acid metabolism in these cells. Interestingly, a previous study using \u003cem\u003eTARDBP\u003c/em\u003e knockdown in iPSC-derived motor neurons and HeLa cells found reduced ATP-linked respiration and impaired mitochondrial function, reduced \u003cem\u003ePFKP\u003c/em\u003e expression, and no alteration in TGs [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. While our data also show reduced \u003cem\u003ePFKP\u003c/em\u003e expression, the increase in TGs appears microglia-specific, suggesting unique metabolic adaptations across cell types. Additional studies examining substrate utilization in \u003cem\u003eTARDBP\u003c/em\u003e knockdown microglia and patient-derived microglia will be crucial in elucidating the mechanisms underlying TG heterogeneity.\u003c/p\u003e\u003cp\u003eLastly, we were able to show that MDMi derived from individuals with \u003cem\u003eTARDBP\u003c/em\u003e mutations recapitulated some key phenotypes of our \u003cem\u003eTARDBP\u003c/em\u003e knockdown MDMi, including LD accumulation, increased IL1β, and response to DGAT inhibitors. This provides an exciting, new understanding of metabolic alterations that could drive ALS pathology in a TDP-43-dependent manner. MDMi derived from sALS patients also displayed significant increases in IL1β and accumulation of large LDs, although mean LD intensity was reduced. 90% of sALS patients exhibit TDP-43 pathology, and these phenotypes could therefore be driven by TDP-43 to some extent. However, gene expression analysis showed higher expression of fatty acid and triglyceride metabolism genes in the TDP-ALS MDMi compared to sALS MDMi, specifically, \u003cem\u003eDGAT2\u003c/em\u003e was increased in the TDP-ALS MDMi and not sALS MDMi (in addition to \u003cem\u003eFABP4\u003c/em\u003e, \u003cem\u003eFADS1\u003c/em\u003e, \u003cem\u003eFASN\u003c/em\u003e, and \u003cem\u003eACSL4\u003c/em\u003e) although only \u003cem\u003eFASN\u003c/em\u003e was significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Measuring TDP-43 nuclear depletion by immunohistochemistry in TDP-ALS compared with sALS MDMi would enable a better understanding of whether the differences are driven by TDP-43 nuclear depletion. It is important to note that although DGAT inhibitors reduced LipidTox intensity, they did not reduce LD size in TDP-ALS MDMi, suggesting that while blocking DGAT enzymes may reduce the number of LDs, the existing triglycerides may be stored in fewer LDs, making them bigger. Additionally, DGAT1 is thought to be involved in the formation of new LDs, while DGAT2 is involved in expansion of existing LDs [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. The inhibitors may have a disproportionate effect on these enzymes, causing greater inhibition of DGAT1 compared to DGAT2.\u003c/p\u003e\u003cp\u003eInterestingly, other immune-related genes like \u003cem\u003eTREM2\u003c/em\u003e and \u003cem\u003eCCL4\u003c/em\u003e showed distinct alterations in the TDP-ALS MDMi compared to the knockdown. \u003cem\u003eTREM2\u003c/em\u003e was slightly upregulated, while \u003cem\u003eCCL4\u003c/em\u003e was downregulated. This could be due to a difference in the stage of disease captured by both models or could reflect heterogeneity in ALS subtypes. When comparing to the shRNA knockdown, genes related to carbohydrate metabolism and cholesterol biosynthesis were both increased in the TDP-ALS MDMi, which contrasts with the \u003cem\u003eTARDBP\u003c/em\u003e knockdown MDMi. This could be a compensatory effect of reduced function or suggest alternate mechanisms of lipid dysfunction in both models. Functional assays to measure glycolysis and cholesterol synthesis would therefore be required to elucidate how the knockdown model differs from patient-derived MDMi and the extent to which gene expression may be correlated with phenotypes such as TG and cholesterol levels in these cellls.\u003c/p\u003e\u003cp\u003eOur study has certain limitations to be acknowledged. While several methods exist to model human microglia, we used MDMi, which undergo a similar polarization step as iPSC-derived microglia, but are more adept at incorporating human variability driven by age, disease state, and natural heterogeneity [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. Although this heterogeneity reflects real-world diversity, future studies should employ a larger sample size to determine whether there are genotype-specific effects. Further, all our analyses were performed at Day 14 post-differentiation, which may represent a late-stage response to \u003cem\u003eTARDBP\u003c/em\u003e knockdown since the shRNA treatment is done on Day 4. Additionally, the observed increase in \u003cem\u003eATGL\u003c/em\u003e and \u003cem\u003eHSL\u003c/em\u003e expression and decrease in \u003cem\u003eDGAT1\u003c/em\u003e and \u003cem\u003eDGAT2\u003c/em\u003e expression in the knockdown model, despite elevated triglycerides, suggests that microglia may attempt to counteract triglyceride accumulation over time. Time-course experiments could clarify whether early-stage metabolic adaptations differ from later responses.\u003c/p\u003e\u003cp\u003eOur targeted gene expression approach with Fluidigm analysis, while informative, does not capture the full transcriptomic landscape. Unbiased RNA sequencing could provide a more comprehensive view of \u003cem\u003eTARDBP\u003c/em\u003e knockdown effects and also uncover important differences between the shRNA knockdown model and the ALS patient-derived MDMi model. Additionally, lipidomic analysis of isolated lipid droplets from a greater sample size to understand the full profile of triglyceride, cholesterol and fatty acid species would help validate some of our data. With regards to the use of shRNA-mediated lentiviral knockdown, while this is effective and provides a very stable knockdown allowing for downstream assays, alternative approaches such as siRNA or CRISPR-mediated knockdown in iPSC-derived microglia could further validate our findings. Our knockdown reduced protein levels by 20\u0026ndash;30%, which is low compared to other studies that use iPSC-derived microglia or cell lines that allow for more efficient knockdown, however, even with a modest knockdown, we do see a large biological effect. Nevertheless, using these alternate approaches would also provide larger cell counts to perform activity assays and western blots to confirm gene expression data. Lastly, while we were able to obtain three TDP-ALS and nine sALS patient samples, cell yields were insufficient to perform nuclear TDP-43 staining or functional assays such as phagocytosis and response to LPS stimulation. Future studies with a larger cohort of patient-derived samples will be essential to fully elucidate how lipid pathways are altered in ALS and how these changes impact immune function. RNA sequencing and splicing analysis would also potentially uncover novel targets by which TDP-43 could directly modulate bioenergetic and triglyceride pathways in microglia.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study demonstrates that nuclear depletion of TDP-43 in MDMi leads to metabolic and immune alterations, characterized by increased fatty acid uptake, triglyceride accumulation, and possible impairment in glycolysis. These changes are accompanied by pro-inflammatory cytokine production, independent of cytoplasmic TDP-43 aggregation. The findings highlight the importance of microglial lipid metabolism in neuroinflammation and suggest that triglyceride accumulation may drive microglia activation in ALS. Further studies on lipid droplet heterogeneity and metabolic adaptations in microglia could provide new insights into ALS pathogenesis and identify potential therapeutic targets.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMDMi Cell Culture\u003c/span\u003e: MDMi are created as described previously [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e]. Blood from healthy human donors is separated using a density gradient medium Lymphoprep (Stemcell technologies #07851) to isolate mononuclear cells. These peripheral blood mononuclear cells (PBMCs) are cryopreserved in Fetal Bovine Serum (FBS) with 10% Dimethyl sulfoxide (DMSO) at -80\u003csup\u003eo\u003c/sup\u003eC until needed. PBMCs are thawed and monocytes are obtained through CD14\u0026thinsp;+\u0026thinsp;microbead isolation (Miltenyi #130-050-201). Monocytes are then plated in 96 well plates at a density of 200,000 cells per well and cultured in serum-free RPMI (Gibco #R8758) media with 1% penicillin and streptomycin (10,000 U/mL) (Fisher Scientific 15-140-122) and 2.5 \u0026micro;g/mL Fungizone (Cytiva #SV30078.01). A cytokine cocktail consisting of macrophage colony-stimulating factor (M-CSF) (10 ng/mL), granulocyte-macrophage colony-stimulating factor (GM-CSF) (10 ng/mL), nerve-growth factor-b (NGF-b) (10 ng/mL), chemokine ligand 2 (CCL2) (100 ng/mL), and interleukin-34 (IL-34) (100 ng/mL), is added to the media. Cells are differentiated into microglia-like cells (MDMi) over 10 days with the help of these cytokines. Cytokines were purchased from R\u0026amp;D Systems (NGF-b, GM-CSF, and IL-34) and Biolegend (M-CSF and CCL2). Monocyte-derived microglia-like cell models have been reviewed and characterized as an appropriate model to study human microglia \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR125 CR126\" citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e]. Assays were done using MDMi from 3\u0026ndash;6 individuals per experimental run. For each assay, 2 to 4 batches (repeat experimental runs) were done and the total number of individuals used per assay is in the figure legend.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePreparation of shRNA lentiviral particle\u003c/span\u003e: Lentiviral particles were prepared as previously described [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. Briefly, on day 1, 293T cells were transfected using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA, United States) with packaging and envelope plasmids (Vpx cDNA and pHEF-VSVG). On day 2, 293T culture media was replaced with RPMI-1640 Glutamax (Invitrogen, Waltham, MA, United States) containing 1% fungizone (Amphotericin B) and 1% penicillin/streptomycin. After 48 hours, lentiviruses containing the Vpx particles were harvested, centrifuged for 5 minutes at 400xg and the supernatant collected. The supernatant was filtered using a 0.45-\u0026micro;m syringe filter (EMD Millipore, Burlington, MA, United States). Lentiviral particles containing targeted shRNA for each gene were obtained from Milipore Sigma (TARDBP construct: TRCN0000016038, Target Sequence: GCTCTAATTCTGGTGCAGCAA).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLentiviral mediated knockdown of MDMi\u003c/span\u003e: For the transduction of MDMi cells, on day 4 of differentiation, the culture media was replaced with 100 \u0026micro;l of Vpx-VLP and 100\u0026micro;L of fresh RPMI media containing 2X concentration of cytokines. After 2\u0026ndash;3 hours, 10 \u0026micro;l TRC virus-containing shRNA or scramble control (Sigma) was added to each well. On day 7, puromycin (Life Technologies, Carlsbad, CA, United States) at a concentration of 3 \u0026micro;g/ml was added to eliminate non-transduced cells. On day 10, MDMi were lysed for RNA isolation [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. A\u0026thinsp;\u0026gt;\u0026thinsp;50% knockdown of RNA (by qPCR) was considered optimal for the experiment (higher knockdown efficiencies could not be obtained for the \u003cem\u003eTARDBP\u003c/em\u003e gene, and this level of knockdown showed significant protein level reduction, so it was considered sufficient to study effects of \u003cem\u003eTARDBP\u003c/em\u003e reduction). The knockdown was performed with PBMCs from 20 individuals (11 male, 7 female, and two unknown, aged 18 to 70). Of these, 5 samples were removed from analysis due to insufficient knockdown (\u0026lt;\u0026thinsp;50%). Information on the age, sex and ethnicity of these individuals are provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCholesterol uptake, fatty acid uptake and lipid droplet staining\u003c/span\u003e: A fluorescent cholesterol analog, NBD-cholesterol (22‐(N‐(7‐Nitrobenz‐2‐oxa‐1,3‐Diazol‐4‐yl)Amino)‐23,24‐Bisnor‐5‐Cholen‐3β‐OI) (ThermoFisher N1148) was used to determine cholesterol uptake. Cells were incubated with NBD-cholesterol for 1 hour, followed by fixing with 4% paraformaldehyde (PFA). Fluorescence was quantified using images from confocal microscopy or by plate reader measurements (TECAN Infinite 200 PRO). Fatty acid uptake was measured by incubating cells in a similar manner with 20 \u0026micro;M C-12 BODIPY labeled fatty acid (ThermoFisher D3823). Staining of lipid droplets was performed using HCS LipidTox\u0026trade; Deep Green or Red neutral lipid stain (ThermoFisher H34475) according to manufacturer instructions. Zeiss LSM 900 was used for confocal microscopy and images were quantified using CellProfiler. For plate reader measurements, 16 readings were taken per well covering different areas within the well, and mean values were plotted.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTotal Cholesterol/Free cholesterol Assay\u003c/span\u003e: Total and free cholesterol levels were determined using the Amplex Red cholesterol assay (ThermoFisher A12216) as per the manufacturers protocol. Cells were grown on 12-well plates, and after the knockdown protocol on day 10, media containing viral particles was replaced with fresh RPMI media without cytokines. After 3 days, cells were collected using a cell scraper, and frozen at -80\u003csup\u003eo\u003c/sup\u003eC in 250 \u0026micro;L water. Supernatants were also collected and frozen. When ready to perform the assay, cells were thawed, and the protein was quantified using Bradford\u0026rsquo;s assay. Lipid extraction was done by adding chilled chloroform and methanol in a 2:1 ratio. This was followed by vortexing for 30 seconds and 2-minute incubation on ice, repeated 3 times. The samples were then centrifuged at 14000rpm for 10 minutes, followed by transferring the lower organic layer to a new Eppendorf tube. This was placed under nitrogen until all the chloroform was dried, followed by resuspension of the lipids in the reaction buffer provided in the kit. Cholesterol esters were calculated by subtracting free cholesterol from total cholesterol. All values were normalized to protein levels measured with Bradford\u0026rsquo;s assay.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTriglyceride/Glycerol Assay\u003c/span\u003e: Triglyceride-Glo Assay (Promega J3160) was used to measure total glycerol and free glycerol in cells and supernatants, as per the manufacturer\u0026rsquo;s instructions. Cells were seeded in 96-well plates in duplicate (to perform the assay with and without lipase). On Day 10, media containing viral particles was replaced with fresh RPMI media without cytokines. After 3 days incubation in fresh RPMI, on Day 14, the supernatant was removed and collected, and cells were washed once with PBS, followed by the addition of the glycerol lysis buffer (as per kit instructions). Cells and supernatants were assayed at the same time, and luminescence was measured by a plate reader. Triglyceride levels were calculated by subtracting free glycerol from total glycerol. An equal number of cells plated per well serves as normalization, as protein quantification could not be performed with this kit.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eImmunohistochemistry\u003c/span\u003e: MDMi were plated in 24-well plates with glass coverslips. On Day 10, cells were washed 3X with 3% BSA and 0.1% TritonX in PBS, fixed with 4% PFA for 15 minutes, washed again, and incubated with primary antibodies (Goat Anti-Iba1, Fujifilm CAT# 011-27991 and Rabbit Anti-TDP43, R\u0026amp;D Systems, CAT# MAB7778, Rabbit Anti- phospho-TDP43, Proteintech, CAT#22309-1-AP) overnight at 4\u003csup\u003eo\u003c/sup\u003eC. The next day cells were washed 3X with PBS followed by incubation with the appropriate Alexa-conjugated secondary antibodies for 1 hour at RT. Cells were washed again 3X, and the glass coverslips were then mounted on microscope slides using FluoroG mounting solution and imaged at 60X using Zeiss LSM900 confocal microscopy.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWestern Blot Analysis\u003c/span\u003e: Cell protein concentrations were measured using the Quick Start Bradford Protein Assay Kit 1 (Bio-Rad 5000201) in a Tecan Infinite F200 PRO spectrophotometer. 10 \u0026micro;g protein was combined with 4X Laemmli loading buffer in a final volume of 30 \u0026micro;L, heated at 95\u003csup\u003eo\u003c/sup\u003eC and loaded on an Invitrogen 4\u0026ndash;20% tris\u0026ndash;glycine SDS-PAGE gel. Electrophoresis was conducted at 80\u0026ndash;120 V using standard tris\u0026ndash;glycine running buffer. The sample was transferred to an Immuno-Blot PVDF membrane (Bio-Rad 1620177) in standard tris\u0026ndash;glycine transfer buffer with 20% methanol and 0.04% SDS at 150 mA for 2 h in a wet transfer. The primary antibodies used are as follows: Anti-TDP43, R\u0026amp;D systems, CAT# MAB7778, Anti-GAPDH Cell Signaling, CAT#5174S.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDextran Uptake Assay\u003c/span\u003e: Cells were incubated with 0.1 mg/mL Dextran Alexa Fluor\u0026trade; 647 10,000 MW (ThermoFisher D22914) for 1 hour at 37\u003csup\u003eo\u003c/sup\u003eC, followed by fixation with 4% PFA and imaging with a confocal microscope. As a positive control, cells were incubated with 10\u0026micro;M Cytochalasin D (FisherSci., Cat# 12\u0026ndash;331) for 20 minutes prior to incubation with Dextran. Image analysis was done using CellProfiler.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCell Lysis and RNA Isolation\u003c/span\u003e: MDMi are lysed with RLT buffer (Qiagen #74104) with 1:100 β-mercaptoethanol, purified RNA is extracted using the RNeasy 96 well plate isolation kit (Qiagen #74182). Reverse Transcriptase PCR and qPCR: RNA is transformed to cDNA using reverse transcription PCR. The PCR mix consists of dNTP mix (#R72501), Random Hexamers (#N8080127), RNase Inhibitor (#N8080119), MgCl\u003csub\u003e2\u003c/sub\u003e (#AB0359), 10x PCR buffer (#4486220), and M-MLV Reverse Transcriptase (#28025013) purchased from Thermofisher Scientific. Reagents and RNA are loaded into a 96-well plate to a total volume of 50\u0026micro;L and run in an Applied Biosystems MiniAmp Thermocycler. Thermocycler program: 25\u003csup\u003eo\u003c/sup\u003eC for 10 minutes, 48\u003csup\u003eo\u003c/sup\u003eC for 45 minutes, 95\u003csup\u003eo\u003c/sup\u003eC for 5 minutes, and held at 4\u003csup\u003eo\u003c/sup\u003eC upon run completion.\u003c/p\u003e\u003cp\u003eSample cDNA is loaded into a 96-well PCR plate with Taqman Fast Advanced Mastermix (#4444554), assay primer for the target gene to be detected with FAM, and housekeeping gene to be detected with VIC. qPCR primers were purchased from Thermofisher Scientific. For all experiments, the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used to normalize all values as relative expression (R.E.). Samples are run with the protocol: Stage 1 (x1): 50\u003csup\u003eo\u003c/sup\u003eC for 2 minutes, 95\u003csup\u003eo\u003c/sup\u003eC for 2 seconds. Stage 2 (40x): 95\u003csup\u003eo\u003c/sup\u003eC for 1 second, 60\u003csup\u003eo\u003c/sup\u003eC for 20 seconds. Cycle threshold (Ct) values are collected and normalized to GAPDH Ct.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMicrofluidic qPCR Analysis\u003c/span\u003e: Gene expression analysis was performed by parallel qPCR using the high-throughput Fluidigm BioMark HD platform (Standard BioTools, San Francisco, CA, USA), according to the manufacturer's instructions. GAPDH and TUBB were used as reference genes, but final data are reported using GAPDH as a reference. A pre-amplification step was included to increase the number of cDNA copies to a detectable level and to allow the concurrent amplification of the different gene expression targets. The Fluidigm IFC was primed with control line fluid on the IFC controller. Subsequently, assay and sample mixes were loaded on the IFC and placed on the controller which pressure-loaded the assay components into the reaction chambers. The IFC was then placed on the Biomark HD for thermocycling and fluorescence detection. The data was reviewed on the Fluidigm Real Time analysis software. Data from multiple Fluidigm runs was normalized by calculating fold change of the knockdown over scramble control. Multiple paired t-test was used to determine significance of alterations, and this was used to make a volcano plot. Although Fluidigm analysis can only analyze 96 genes simultaneously, our data includes 110 genes, as multiple Fluidigm runs were combined, and not all genes were run on every sample.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eConfocal Microscopy\u003c/span\u003e: Cells were imaged on the Zeiss LSM 900 confocal microscope. Images were processed using FIJI image processing software and analyzed using the open-source program CellProfiler to classify and count cells and measure staining intensity. For CellProfiler, customized pipelines were developed to analyze lipid droplet intensity, TDP-43 localization and dextran uptake [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Compactness was measured using the \u0026ldquo;MeasureObjectShapeSize\u0026rdquo; module, where compactness is calculated as \u0026ldquo;the mean squared distance of the object's pixels from the centroid divided by the area\u0026rdquo;. A filled circle will have a compactness of one, whereas flatter or more elongated and ramified cells will have a compactness greater than one.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCellProfiler Pipelines\u003c/span\u003e: Imaging analysis pipelines involved identifying cells as a primary object using DAPI stain, setting appropriate thresholds for the nucleus size. CellMask staining was used to identify the cell body as a secondary object. Cells were filtered to only include those with a nucleus (using \u0026ldquo;FilterObject\u0026rdquo; and \u0026ldquo;RelateObject\u0026rdquo; modules), followed by measuring the average intensity per cell using \u0026ldquo;MeasureObjectIntensity\u0026rdquo; for LipidTox staining, NBD-cholesterol, or Dextrain staining. To measure TDP-43 nuclear and cytoplasmic staining, Hoechst staining was used to identify the nucleus, and Iba1 staining was used to identify the cell body. The \u0026ldquo;MaskObject\u0026rdquo; module was used to identify cytoplasmic area (without nucleus). Mean values per image were used for final analysis in all imaging analysis except ALS patient-derived MDMi LD analysis, where a nested analysis was performed. For each well, 5\u0026ndash;15 images were obtained and analyzed, with each image containing 20\u0026ndash;100 cells.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCellMask Stain\u003c/span\u003e: For lipid droplet staining and dextran uptake assays, CellMask (Invitrogen CAT#C10045) was used to stain the plasma membrane before fixing cells. Briefly, cells were incubated with CellMask (1:1000) for 5\u0026ndash;10 minutes at 37\u003csup\u003eo\u003c/sup\u003eC in RPMI media, followed by washing 3X with PBS and fixing with 4% PFA.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDrug Inhibitors\u003c/span\u003e: DGAT 1 and 2 inhibitors (Sigma PF04620110 and PF06424439 respectively) were added simultaneously at a concentration of 5uM. To inhibit cholesterol esterification, 20 \u0026micro;M 3-[Decyldimethylsilyl]‐N‐[2‐(4‐methylphenyl)‐1‐phenethyl] propanamide (Sandoz 58‐035), a specific ACAT1 inhibitor, was used. To inhibit fatty acid uptake via CD36 inhibition, we used Sulfosuccinimidyl Oleate (sodium salt), or SSO (Cayman Chemical # 1212012-37-7) at a concentration of 20 \u0026micro;M. For all drug treatments, MDMi media was changed on day 10 (after lentiviral knockdown) to remove virus from the media. After 3 days, inhibitors were added for 12\u0026ndash;16 hours, and the next day (day 14) cells were assayed. DMSO was used as the vehicle control.\u003c/p\u003e\n\u003ch3\u003eLipidomic Analysis:\u003c/h3\u003e\n\u003cp\u003eSamples were sent to the Lipidomics Core facility at Columbia University Medical Center. Lipids were extracted from equal amounts of material (1\u0026nbsp;million cells per sample). Lipid extracts were prepared via chloroform\u0026ndash;methanol extraction, spiked with appropriate internal standards, and analyzed using a 6490 Triple Quadrupole LC/MS system (Agilent Technologies, Santa Clara, CA) as described previously [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical Analysis\u003c/span\u003e:\u003c/h2\u003e\u003cp\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM with statistical significance being determined as \u003cem\u003eP\u003c/em\u003e values generated with a 95% confidence interval. All paired t-tests were 2-tailed, assuming normal (Gaussian) distribution. Multiple paired t-tests were performed for Fluidigm data analysis of knockdown MDMi, and an adjusted p-value of \u0026lt;\u0026thinsp;0.05 (correcting for multiple comparisons using the Holm-Sidak test) was considered significant. Unadjusted p-values from multiple unpaired t-tests (2-tailed, Gaussian distribution) were used to analyze Fluidigm data for ALS patient-derived MDMi due to low sample numbers. DGAT-treated samples were compared to untreated samples using paired analyses. Statistical analysis on lipidomics data was performed using 2-tailed paired t-tests. All statistical analysis was performed in GraphPad Prism. To denote significance GP style annotation is used: ns not significant; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*\u003cem\u003e), p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (**), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (***\u003c/em\u003e), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 (****).\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACAT1: Acyl-coenzyme A:cholesterol acyltransferase-1\u003c/p\u003e\n\u003cp\u003eAD: Alzheimer’s Disease\u003c/p\u003e\n\u003cp\u003eALS: Amyotrophic lateral sclerosis\u003c/p\u003e\n\u003cp\u003eDAM: Disease-Associated Microglia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDGAT: Diacylglycerol Acyltransferase\u003c/p\u003e\n\u003cp\u003eLD: Lipid Droplet\u003c/p\u003e\n\u003cp\u003eMDMi: Monocyte-Derived Microglia-Like Cells\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the participants of the New York Blood Center (NYBC) and the Eleanor and Lou Gehrig ALS Center at Columbia University for their contribution to this research. All participants were recruited and consented to participate and donate samples for research related to neuromuscular conditions under Columbia IRB Protocol # AAAK2000. This work was supported by the US National Institutes of Health grants R21AG073882, RF1AG058852, and R01AG076018 (EMB) and the Department of Defense grant AL200097 (WE).\u0026nbsp;The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. EMB and WE are current Ludwig Scholars in the Carol and Gene Ludwig Center for Research in Neurodegeneration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAuthor Contributions\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.K. and E.M.B. implemented the study and wrote the manuscript. E.A.G analyzed and interpreted lipidomics data. D.D. made PCA plots and heatmaps for Fluidigm data analysis. R.T helped with the analysis of confocal imaging data using CellProfiler. M.Y. and R.T. processed the NYBC and ALS blood samples used in the study. B.H., O.R. N.S. and W.E. supplied cryopreserved PBMCs from a thoroughly characterized ALS cohort. All authors read and edited the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarberio J, Lally C, Kupelian V, Hardiman O, Flanders WD. Estimated Familial Amyotrophic Lateral Sclerosis Proportion: A Literature Review and Meta-analysis. Neurol Genet 2023;9:e200109. https://doi.org/10.1212/NXG.0000000000200109.\u003c/li\u003e\n\u003cli\u003eAlsultan A, Waller R, \u0026hellip; PH-D neurological, 2016 undefined. The genetics of amyotrophic lateral sclerosis: current insights. 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NatureComM Ohgidani, TA Kato, D Setoyama, N Sagata, R Hashimoto, K Shigenobu, T YoshidaScientific Reports, 2014\u0026bull;natureCom n.d. https://doi.org/10.1038/srep04957.\u003c/li\u003e\n\u003cli\u003eConnor SM, Rashid M, Ryan KJ, Patel K, Boyd JD, Smith J, et al. GW5074 Increases Microglial Phagocytic Activities: Potential Therapeutic Direction for Alzheimer\u0026rsquo;s Disease. Front Cell Neurosci 2022;16:894601. https://doi.org/10.3389/FNCEL.2022.894601/BIBTEX.\u003c/li\u003e\n\u003cli\u003eKruti A, Patel R, Zhu K, Henrion MYR, Beckmann ND, Moein S, et al. Single Cell-type Integrative Network Modeling Identified Novel Microglial-specific Targets for the Phagosome in Alzheimer\u0026rsquo;s disease. BioRxiv 2020:2020.06.09.143529. https://doi.org/10.1101/2020.06.09.143529.\u003c/li\u003e\n\u003cli\u003eChan R, Oliveira T, Cortes E, \u0026hellip; LH-J of B, 2012 undefined. Comparative lipidomic analysis of mouse and human brain with Alzheimer disease. ElsevierRB Chan, TG Oliveira, EP Cortes, LS Honig, KE Duff, SA Small, MR Wenk, G ShuiJournal of Biological Chemistry, 2012\u0026bull;Elsevier n.d.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TDP-43, Lipid Droplets, Triglycerides, ALS, Neurodegeneration, Microglia, MDMi","lastPublishedDoi":"10.21203/rs.3.rs-8036170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8036170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease marked by progressive motor neuron loss, with TDP-43 pathology present in over 90% of cases. While neuroinflammation is a recognized hallmark, the role of microglia in ALS pathogenesis remains incompletely understood. Here, we demonstrate that TDP-43 regulates microglial function via triglyceride metabolism. Using shRNA-mediated \u003cem\u003eTARDBP\u003c/em\u003e knockdown in human monocyte-derived microglia-like cells (MDMi), we observed suppressed cholesterol biosynthesis, upregulated fatty acid uptake, lipid droplet accumulation, enhanced phagocytic activity, and increased IL-1β production. Inhibiting diacylglycerol acyltransferase (DGAT) enzymes reduced lipid droplet formation, phagocytosis, and IL-1β, directly linking the triglyceride pathway to microglial activation. Patient-derived MDMi from both sporadic and \u003cem\u003eTARDBP\u003c/em\u003e-mutant ALS cases showed overlapping as well as distinct alterations, some of which were reversed by DGAT inhibition. Our findings identify dysregulated triglyceride metabolism as a novel pathway through which TDP-43 mediates microglial dysfunction, highlighting a potential therapeutic target for ALS.\u003c/p\u003e","manuscriptTitle":"Loss of Nuclear TDP-43 Impairs Lipid Metabolism in Microglia-Like Cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 11:33:24","doi":"10.21203/rs.3.rs-8036170/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8489ab39-fdc9-4471-b418-06fa5f2291ff","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T16:11:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 11:33:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8036170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8036170","identity":"rs-8036170","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}