The Alzheimer’s disease risk gene SORL1 is a regulator of excitatory neuronal function

The Alzheimer’s disease risk gene SORL1 is a regulator of excitatory neuronal function | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The Alzheimer’s disease risk gene SORL1 is a regulator of excitatory neuronal function C. Andrew Williams , Shannon E. Rose , Vera Stamenkovic , View ORCID Profile Stephen E.P. Smith , View ORCID Profile Jessica E. Young doi: https://doi.org/10.1101/2025.07.28.667194 C. Andrew Williams 1 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA, USA 2 Institute for Stem Cell and Regenerative Medicine, University of Washington School of Medicine , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shannon E. Rose 1 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA, USA 2 Institute for Stem Cell and Regenerative Medicine, University of Washington School of Medicine , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vera Stamenkovic 3 Center for Integrative Brain Research, Seattle Children’s Research Institute , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephen E.P. Smith 3 Center for Integrative Brain Research, Seattle Children’s Research Institute , Seattle, WA, USA 4 Graduate Program in Neuroscience, University of Washington , Seattle, WA, USA 5 Department of Pediatrics, University of Washington School of Medicine , Seattle, WA USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephen E.P. Smith Jessica E. Young 1 Department of Laboratory Medicine and Pathology, University of Washington School of Medicine , Seattle, WA, USA 2 Institute for Stem Cell and Regenerative Medicine, University of Washington School of Medicine , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica E. Young For correspondence: jeyoung{at}uw.edu Abstract Full Text Info/History Metrics Preview PDF Abstract Background Synaptic dysfunction is an early feature of Alzheimer’s disease (AD) and a significant contributor to cognitive decline and neurodegeneration. Proper localization of proteins involved in pre-and post-synaptic composition is dependent on endosomal recycling and trafficking. Alterations in trafficking complexes, such as retromer, have been shown to impair neuronal synaptic function. The SORL1 gene has been strongly implicated in AD pathogenesis and its protein product, SORLA, is an endosomal receptor that works in conjunction with retromer to regulate endosomal recycling. Methods We utilized our established human induced pluripotent stem cell (hiPSC) derived excitatory cortical neuron model to examine SORL1 ’s role in synaptic protein composition and neuronal function. We used Quantitative Multiplex co-Immunoprecipitation (QMI), a mesoscale proteomics assay to measure synaptic protein interactions, immunocytochemistry to assay synapses and AMPA receptor subunits, and multi-electrode arrays (MEAs) to measure neuronal function of SORL1 KO and isogenic control hiPSC derived neurons. Results We show that loss of SORL1 expression significantly changes many synaptic protein-protein interactions and patterns of expression. We demonstrate that SORL1 deficient neurons are hyperactive and that the increased activity is driven by glutamatergic neurotransmission. Hyperexcitability has been seen in other models of AD with familial AD variants in amyloid precursor protein and presenilin genes, due to the increases in amyloid beta (Aβ) peptides. In the case of SORL1 deficiency, the hyperexcitability we observe is primarily due to mis-trafficking of synaptic proteins, rather than an overall increase in Aβ. Finally, we find that SORL1 deficient neurons have impaired synaptic plasticity. Conclusions These findings further support a growing body of literature implicating early endosomal recycling defects as drivers of AD pathogenesis. Furthermore, our work supports further emphasis on exploring the SORL1-retromer pathway for therapeutic development in AD. Background Alzheimer’s disease (AD) is characterized by progressive synapse loss and dysfunction that precedes widespread neuronal death and correlates strongly with cognitive decline ( Tzioras 2023 ). Synaptic alterations include changes in synapse morphology, impaired synaptic plasticity and disrupted neurotransmission, particularly affecting glutamatergic neurons in the cerebral cortex ( Griffiths and Grant 2023 ). Hallmark AD pathogenic proteins amyloid beta (Aβ) and hyper-phosphorylated tau (pTau) interfere with synaptic function by impairing long-term potentiation and disrupting synaptic transmission ( John and Reddy 2021 ) but how AD risk genes impact synapses is largely unknown. Pathogenic changes to neuronal synapses and disruption of signaling can lead to aberrant neuronal activity and larger scale network activity dysfunction. Neuronal hyperactivity is associated with early disease state, followed by hypoactivity with advancing disease status. Disease-associated hyperactivity can manifest as an increased prevalence of seizures and epileptiform brain activity ( Vossel 2017 ) and is associated with faster rates of cognitive decline. Neuronal hyperexcitability is seen in multiple models of AD ( Targa Dias Anastacio 2022 ) and has emerged as a significant contributor to synaptic dysfunction. This hyperexcitability manifests as increased spontaneous neural activity, altered calcium homeostasis and enhanced susceptibility to excitotoxicity ( Targa Dias Anastacio 2022 ), altered glutamate receptors, and changes to excitatory-inhibitory balance ( Lerdkrai 2018 , Ghatak 2019 ). Ultimately, this cascade of events can lead to synaptic exhaustion and degeneration through excessive metabolic demands and increased cellular stress ( Katsu-Jimenez 2017 , Guo 2017 ). Deficiencies in endosomal recycling can contribute to synaptic dysfunction. Reduced expression of the retromer complex, a multiprotein complex heavily involved in endosomal trafficking and recycling, lead to loss of cell-surface glutamatergic AMPA receptors and impaired long-term potentiation ( Temkin 2017 , Simoes 2021 ). The sortilin-related receptor SORLA ( SORL1) directly interacts with retromer to facilitate endosomal through the recycling, retrograde, and degradative pathways ( Barthelson 2020 ). SORL1 is an established AD risk gene, implicated by multiple GWAS studies ( Kim 2025 , Bellenguez 2017 , Lambert 2013 ) and by exome studies showing that loss of function variants in SORL1 are causal for early onset AD ( Holstege 2017 ). Furthermore, SORLA protein levels are decreased in the AD human brains and specifically decreased in the AD-susceptible trans- entorhinal cortex ( Simoes 2021 ). We have previously established models of SORL1 deficiency using human induced pluripotent stem cell (hiPSC)-derived neurons and glia ( Knupp 2020 , Mishra 2022 , Mishra 2025 ). In cortical neurons with loss of SORL1 expression we have observed altered trafficking of key proteins involved in neuronal synaptic function including the GLUR1 subunit of the AMPA receptor and the BDNF receptor TRKB ( Mishra 2022 ). However, how SORL1 expression shapes neuronal function and synapses remains underexplored. Here, we used SORL1 deficient human cortical neurons to investigate the role SORL1 plays in synaptic structure and neuronal activity. We report that SORL1 KO neurons have disrupted synaptic protein interactions and alterations of excitatory postsynaptic proteins and synapses. We find that excitatory glutamate AMPA receptor subunits have altered expression profiles in SORL1 KO neurons. We observe that SORL1 deficient neurons are hyperexcitable relative to isogenic wild-type neurons and that the increased neuronal activity is driven principally by glutamate excitatory neurotransmission. This hyperexcitability phenotype is not solely due to increased secretion of soluble Aβ peptides. We also demonstrate the SORL1 deficient neurons have impaired long-term potentiation, demonstrating, for the first time, that SORL1 plays a critical role in activity-driven modification of the synapse. Together, our study shows that our human neuron model of SORL1 deficiency recapitulate an early phenotype of AD pathology that may represent a novel therapeutic target. Methods Cell lines and CRISPR-Cas9 genome editing All cell lines used in this study have been previously published ( Knupp 2020 , Mishra 2022, Mishra 2023, Shin Evitts 2023 ). All cell lines used are isogenic and generated from the published and characterized CV cell line ( Young 2015 , Levy 2007 ). This cell line is male with an APOE e3/e4 genotype. CRISPR-Cas9 gene editing was used to generate SORL1 KO clones and APP Swe+/+ as described previously ( Knupp 2020 , Young 2018 ). All clones demonstrated normal karyotypes and regularly tested for mycoplasma (MycoScope, AMS Bio). Neuronal differentiation hiPSCs were differentiated into neurons using dual-SMAD inhibition ( Rose 2018 , Knupp 2020 , Mishra 2022, Mishra 2023). Briefly, hiPSCs were plated on Matrigel (Corning cat. #356231) coated culture plates and expanded with mTesR Plus (StemCell cat. #100-0276) until near-confluent. On day 1 of neural induction, cell medium was switched to basal neural maintenance medium (BNMM) with dual-SMAD inhibitors, composed of: 1:1 DMEM/F12 + glutamine (Gibco cat. #11320-033) and Neurobasal (Gibco cat. #21103-049), supplemented with 1% B27 (Gibco cat. #17504-044), 0.5% N2 (Gibco cat. #17502-048), 0.5% GlutaMax (Gibco cat. #35050-061), 0.5% insulin-transferrin-selenium-A (Gibco cat. #51300-044), 0.5% Non-essential amino acids (NEAA) (Thermo Fisher cat. #11140050), 0.2% β-mercaptoethanol (Thermo Fisher cat. #21985023), and dual-SMAD inhibitors 10μM SB-431542 (Biogems cat. #BG6675SKU301) and 0.5μM LDN-193189 (Biogems cat. #BG5537SKU106). Cells were fed daily for seven days. On day eight, cells were dissociated and split with Versene (Gibco cat. #15640066). One day after passaging, medium was switched to BNMM supplemented with 20ng/ml fibroblast growth factor (FGF) (R&D Systems cat. #233-FB/CF). Cells were passaged and split on day sixteen, continuing to be fed with BNMM+FGF. On approximately day twenty-three, cells were sorted using fluorescence-activated cell sorting (FACS) flow cytometry. Quad-stained cells were isolated for CD24/CD184 positivity and CD44/CD271 negativity( Yuan 2011 ) to isolate neural precursor cells (NPCs). After FACS, NPCs were expanded for neural differentiation. Eight million NPCs were plated on Matrigel-coated 10cm culture plates. After twenty-four hours, medium was switched from BNMM+FGF to BNMM + 0.2 μg/ml brain-derived neurotrophic factor (BDNF) (PeproTech cat. #450-10), 0.2 μg/ml glial-derived neurotrophic factor (GDNF) (Peprotech cat. #450-02), and 500 μM db-cAMP (Sigma-Aldrich #D0627). Media was changed twice a week for three weeks. After three weeks of neuronal differentiation, cells were dissociated with Accutase (Innovative Cell Technologies cat. #AT-104). Cell suspension concentrations were counted using Trypan blue (Gibco cat. #15250-061) and a TC20 cell counter (Bio-Rad). Cells were then replated according to experimental needs. All cells were maintained at 37° C and 5% CO 2 . Quantitative Multiplex Immunoprecipitation (QMI) Following differentiation, neurons were passaged to multiple-electrode arrays (see below) and seven million cells were plated on parallel Matrigel-coated 6 well culture plates. In the first week post plating, cultures were transitioned from BNMM medium containing BDNF, GDNF, and db-cAMP (see above) to Brainphys neuronal medium (Stemcell Technologies cat. #05790) supplemented with 1% B27, 0.5% N2, 0.2 μg/ml BDNF, 0.2 μg/ml GDNF, 500 μM db-cAMP, and 1 μg/ml laminin (Invitrogen cat. #23017-015). Two-thirds of well volume was changed twice a week. MEA plates were monitored for the development of network bursting activity. Upon network bursting detection, approximately 30 days post plating (DPP) after neuronal differentiation, parallel 6 well cultures were harvested for QMI analysis. Multiplexed immunoprecipitation is achieved using antibody-based immunoprecipitations onto color-coded Luminex beads, followed by probing for co-associated proteins using fluorophore-coupled antibodies and quantification on a flow cytometer, as described previously ( Lautz, 2018 , 2021; Heavner 2021 ). Briefly, samples were lysed in NP40 lysis buffer [150mM NaCl, 50mM Tris (pH 7.4), 1% NP-40, 10 mM NaF, 2 mM sodium orthovanadate + Protease/phosphatase inhibitor cocktails (Sigma)] and protein concentrations were measured by BCA assay (Pierce) and normalized between samples. A master mix containing 20 classes of antibody-coupled Luminex beads was prepared and added to lysates. Samples were incubated overnight at 4 °C on a rotator. The following day, beads were washed with cold FlyP buffer [50 mM tris (pH7.4), 100 mM NaCl, 1% bovine serum albumin, and 0.02% sodium azide] and distributed into 40 wells of a 96-well plate for technical replicates. One of 20 biotinylated probe antibodies was added to each well, in duplicate, and the plate was incubated at 4 °C with gentle agitation for 1 hour. After incubation, the resulting bead-probe complexes were washed three times using an automatic plate washer with cold FlyP buffer, incubated with streptavidin-phycoerythrin at 4 °C for 30 min with gentle agitation, and washed an additional three times. Finally, samples were resuspended in 125 μl of ice-cold Fly-P buffer and bead fluorescence was measured from a mean of 60 beads/class/well using a customized refrigerated Bio-Plex 200. For comprehensive video protocol, analysis software code for both R and MATLAB, refer to Brown et al 2019 . QMI validation, including specific validation of each antibody pair, was described previously ( Lautz 2018 ). The full list of IP_ probe antibody pairs used in the study is provided in Table S1 . View this table: View inline View popup Download powerpoint Table S1 Raw data from each well were processed using the BioPlex200’s built-in software to (i) eliminate bead doublets based on doublet discriminator intensity thresholds (>5000 and <25,000 arbitrary units; Bio-Plex 200), (ii) identify specific bead classes according to defined bead regions, and (iii) match individual bead phycoerythrin fluorescence measurements to their corresponding bead regions. This processing generated a distribution of fluorescence intensity values for each pairwise protein interaction measurement. XML output files were parsed to acquire the raw data for use in MATLAB using the ANC program ( Smith 2016 ). ANC/CNA analysis Data from two experiments were input into the ANC program ( Smith 2016 ) as individual batches (N of 4). ANC uses a modified non-parametric Kolmologorv-Smirnoff test to identify interactions that are significantly different between control and SORL1 KO neurons in >70% experiments at a Bonferroni-corrected p<0.05. Median fluorescent intensity matrices were then exported to R, ComBAT-normalzied (SVA package CIte) and input into a custom CNA script in R (Horvath and Langfelder 2008 , Brown 2019 (as above). Artificial variance (<1%) was added for 27 interactions with 0 variance to allow ComBAT to include them, which resulted in no experiment-level batch effects. CNA modules were identified using a soft power of 8 and minimum module size of 10 with a module merge cut-off of 0.4. Modules with a p > 0.05 correlation for the “genotype” variable were considered significant. Interactions that met two independent statistical criteria of 1) individually ANC significant and 2) module membership (p<0.05) in a CNA module that was significantly (p<0.05) associated with the experimental variable “genotype”, were reported in Fig 1 . The heatmap in Fig 1C was generated with the heatmap.2 command in R, and bar graphs in Fig 1D-I were generated using combat-normalized MFIs in Prism (Graphpad). Download figure Open in new tab Figure 1: Widespread alterations in the SORL1 KO glutamate synapse interactome. A) Experimental design. B) Principal component graph of QMI data matrices. C) Heatmap of interactions significantly altered in human SORL1 KO neurons. Each row represents an interaction that was significant by ANC in at least one batch, each column represents a biological replicate. Interaction color indicates the module with which the interaction is most strongly correlated. Bold-italic interactions were significant across both batches. *** indicate interactions shown in D-I. D-I) Median fluorescent intensity (MFI) is shown for 6 example interactions. Circles indicate batch 1, squares batch 2. * indicates ANC-significant (p<0.05) for both batches, # for only batch 2. J) Node-edge diagram showing interactions that were significantly increased (red) or decreased in both batches of human SORL1 KO neurons. Immunocytochemistry Following differentiation, neurons were passaged to Matrigel-coated #1 glass coverslips in 24 well culture plates. Neurons were plated at a density of 500,000 cells per coverslip. In the first week post plating, cultures were transitioned from BNMM medium containing BDNF, GDNF, and db-cAMP (see above) to Brainphys neuronal medium supplemented with 1% B27, 0.5% N2, 0.2 μg/ml BDNF, 0.2 μg/ml GDNF, 500 μM db-cAMP, and 1 μg/ml laminin. Two-thirds of well volume was changed twice a week. At approximately DPP 30, cell medium was removed, coverslips were washed with Dulbecco’s modified phosphate-buffered saline with calcium and magnesium (DPBS+) (Cytiva cat. #SH30264.01) and immediately fixed in 4% paraformaldehyde (PFA) for 15 minutes at room temperature. Following fixation, coverslips were washed three times with DPBS+ to remove PFA. Cells were blocked and permeabilized with 5% normal goat serum (Jackson Laboratories cat. #005-000-121), 2.5% bovine serum albumin (Sigma-Aldrich cat. #A7906), and 0.1% Triton X-100 (Fisher cat. #BP151) for one hour at room temperature. Cells were immediately incubated with primary antibodies at 4°C overnight. HOMER1 (Synaptic Systems cat. #160003 1:250), SYNAPSIN1/2 (Synaptic Systems cat. #106004 1:500), GLUR1 (Millipore cat. # MAB2263 1:500), GLUR2 (ProteinTech cat. #11994-1-AP 1:500), MAP2 (Abcam cat. #ab92434 1:1000). Following primary antibody incubation, coverslips were washed three times with PBS+ with 0.1% Triton X-100. Cells were then stained with secondary antibodies for one hour at room temperature. Secondary antibodies were conjugated to AlexaFluor fluorophores 488, 594, or 647 (Invitrogen) and used at a dilution of 1:1000. Cells were then washed three times with PBS+ to remove unbound secondary antibodies and coverslipped with ProLong Gold mounting medium containing DAPI stain (Invitrogen cat. #P36931) and sealed prior to confocal imaging. Confocal microscopy and image analysis Cells were imaged using a Leica SP8 confocal microscope with a 63x oil immersion objective. All images were captured with the experimenter blinded to cell line. All images were captured and subsequently deconvolved using the Leica LIGHTNING deconvolution method using the Adaptive mode. Captured images had a resolution of 1040 × 1040 pixels with 2x zoom. Laser lines were utilized sequentially. Laser power and detector settings are as follows. For HOMER1 and SYNAPSIN synaptic staining, 405nm (1.00% 80% HyD gain), 488nm (2.00% 800V PMT gain), 552nm (2.00% HyD 243% HyD gain), 638nm (0.1% 840V PMT gain). For GLUR1 and GLUR2 characterization, 405nm (1.00% power, 185% HyD gain), 488nm (1.5% power, 773V PMT gain), 552nm (2.00%, 450% HyD gain), 638nm (0.7% power, 576V PMT gain) settings were used. Z-stacks of synapse puncta were captured with a total Z-size of 5.5 µm in 12 total steps of 0.5 µm. Z-stacks of GLUR1/GLUR2 stained neurons were captured with a total Z-size of 3.25 µm in 17 total steps of 0.2 µm. All imaged neurons were located by MAP2 and DAPI signals. Image analysis of HOMER1/SYNAPSIN and GLUR1/GLUR2 immunostained neurons was performed using Bitplane Imaris software (Oxford Instruments). Similar pipelines were created for both synaptic and glutamate receptor analyses, as follows: The surface object workflow was used to define the MAP2+ neuron regions of interest (whole cell, soma, dendrites) and for the creation of masked HOMER1/SYNAPSIN and GLUR1/GLUR2 channels within the neuron ROIs. The surface object workflows were then created to quantify HOMER1/SYNAPSIN and GLUR1/GLUR2 puncta volume, number, and fluorescent intensity. Colocalization of HOMER1 and SYNAPSIN was analyzed using the ImarisColoc function. Multiple electrode array (MEA) Differentiated neurons were generated as described above and were passaged to Matrigel-coated MEA culture plates in high-density droplets. 6-well and 48-well MEA plates were used for these experiments (Axion Biosystems cats. #M384-tMEA-6W and #M768-tMEA-48W). Matrigel coatings were limited to the area of the recording electrodes. Upon plating, Matrigel droplets were aspirated and neurons were plated on the recording electrodes at a density of 10,000 cells/µl. 300,000 total cells were plated on 6-well MEA plate wells and 80,000 total cells on 48-well MEA plate wells. In the first week post plating, cultures were transitioned from BNMM medium containing BDNF, GDNF, and db-cAMP (see above) to Brainphys neuronal medium supplemented with 1% B27, 0.5% N2, 0.2 μg/ml BDNF, 0.2 μg/ml GDNF, 500 μM db-cAMP, and 1 μg/ml laminin. Two-thirds of well volume was changed twice a week. MEA recordings and analysis Extracellular electrical activity of hiPSC neurons plated on MEA plates was recorded twice a week using Axion Biosystems Maestro Pro system. Recordings were taken prior to medium changes to minimize physical perturbation of neurons. MEA culture plates were acclimated to the MEA reader for approximately ten minutes prior to the onset of recording and the environment was controlled at 37° C and 5% CO 2 . Recordings were approximately four minutes in duration. Signals were recorded at a sampling frequency of 12.5 kHz with a 3 kHz Kaiser window low-pass filter and a 200 Hz high-pass filter. Spikes were detected using the adaptive threshold crossing method of the Axion Axis Navigator software. Electrodes were not included in downstream analyses if they failed to detect more than five spikes a minute. All MEA metrics were calculated from a three-minute length of recording. Mean firing rate and coefficient of variation of the inter-spike interval metrics were calculated using the Axion Neural Metric Tool software. Network bursts were detected and metrics quantified using custom Matlab (Mathworks) scripts in the following manner. Spike times per electrode were first imported into Matlab from Axion .spk files. Spike times across all electrodes were combined and binned with bin sizes of 200ms. Spike time histogram plots of the binned spike times were calculated for each well and a gaussian filter was applied. Network bursts were identified as peaks of the spike time histogram that surpassed an adaptive threshold that was determined by the standard deviation of the overall levels of activity on spike histogram and a minimum spike histogram peak value of thirty action potentials. The start and end timepoints of network bursts were detected by the first intersections of the spike histogram before and after a detected peak and a threshold determined by the median spiking value of the recording. Detected network bursts that did not have spiking activity detected on 20% of electrodes were then discarded. Network bursting, intraburst spiking, and extraburst spiking metrics were then quantified using the start, peak, and end timepoints identified from the spike histogram. Neurotransmitter receptor antagonist experiments Blockade of AMPA, NMDA, and GABA A neurotransmitter receptors were performed pharmacologically with the addition of NBQX (Tocris, cat. #0373), D-AP5 (APV, Tocris cat. #0106), and SR 95531 hydrobromide (Gabazine, Tocris cat. #1262), respectively. A full media change was performed the day before antagonist application. Antagonists were diluted in Brainphys medium at 10x the final concentration. Baseline recordings were performed as described above. After determination of baseline activity, 10x antagonist-containing medium was added to each MEA well for a final antagonist concentration of NBQX 10 µM, APV 50 µM, or Gabazine 10 µM. Recordings were then immediately taken and wells were washed with three full volume medium changes of Brainphys neuronal medium. Approximately two hours later, washout condition recordings were performed. Chemical long-term potentiation (cLTP) cLTP methods were adapted from Pré et al 2022 . Following maturation and development of network bursting, MEA well medium was fully exchanged for Brainphys medium excluding BDNF, GDNF, and db-cAMP. The following day, medium was again fully replaced with medium excluding neurotrophic factors. After two hours, baseline recordings were performed. cLTP induction was performed by the addition of cAMP second messenger pathway modulators forskolin (LC Laboratories cat. #F-9929) and rolipram (Tocris cat. #0905). Medium containing 10x concentrations of forskolin and rolipram were added to MEA wells to give final concentrations of forskolin (50 µM) and rolipram (0.5µM). Wells treated with DMSO vehicle were subjected to identical medium changes as cLTP wells. 30 minutes after cLTP induction, MEA plates were recorded. Following the 30 minute time point, wells were washed with three full medium changes with Brainphys medium without BDNF, GDNF, and db-cAMP. Recordings were performed 4, 24, 48, and 72 hours post cLTP induction. Two-thirds of MEA well medium was removed and replaced following the 48 hour time point recording. cLTP MEA recordings were processed as described above. Due to large changes in intensity and frequency of network bursts, all recordings were reviewed to ensure accurate detection of network bursts. In some cases, manual thresholding of the spike histograms was performed to accurately detect high frequency and low intensity network bursting. Measurements of secreted Aβ 1-40 and Aβ 1-42 Concentrations of secreted Aβ 1-40 and Aβ 1-42 were measured from conditioned medium collected from 48-well MEA culture plates. Neurons were passaged to MEA plates and maintained as described above. Medium was conditioned for 96 hours prior to collection and approximately 150 µl was harvested. MEA electrical activity was recorded and monitored for neuronal firing and network burst development. DPP 14 timepoint was used for preburst Aβ measurements and DPP 35 was used for postbursting Aβ analysis. Secreted Aβ levels were measured using an MSD Aβ V-PLEX ELISA assay (MesoScale Discovery #151200E-2). Two technical replicates per MEA well were measured and final Aβ 1-40 and Aβ 1-42 concentrations were reported as the average of the two replicates. Statistical testing and plotting Statistical testing was conducted in GraphPad Prism software (GraphPad Software). Dataset normality was determined with the D’Agostino and Pearson test. Parametric data were tested using two-tailed unpaired t-tests, paired t-tests, one-way ANOVA tests, or two-way ANOVA tests. Non-parametric data were analyzed with the Kolmogorov-Smirnov test. Tukey’s multiple comparisons testing was performed following one-way ANOVA testing and Sidak’s multiple comparisons testing was performed following two-way ANOVA testing. Plots were generated in R statistical software or GraphPad Prism. Data are presented as mean ± standard deviation (SD) or standard error of the mean (SEM), as appropriate. Representative immunocytochemistry images of analyzed neurons were generated in Imaris and signal brightness levels were optimized for signal visibility. MEA spike time histogram and raster plot traces were generated using Matlab. Results Widespread disruption of protein interactions among AMPA receptors, NMDA receptors, scaffolds and effectors Based on our previous observation of reduced cell surface GLUR1 in SORL1 KO neurons, we hypothesized that altered endosomal recycling due to SORL1 deficiency alters the composition of synaptic proteins in excitatory cortical neurons. After bursting activity was confirmed in parallel cultures by MEA, approximately day post plating (DPP) 35 cultures were lysed and subjected to Quantitative Multiplex co-Immunoprecipitation (QMI), a mesoscale proteomics assay that reports the relative abundance of ∼400 binary interactions among 21 proteins expressed at the excitatory glutamate synapse (Brown 2019, Lautz 2018 , Figure 1A , see Table S1 for proteins and antibodies). Principal component analysis of QMI data matrices showed clear separation of wild-type and SORL1 KO neurons across principal component 1, with additional effects of both batch (two separate differentiations) and cell line (two cell lines in duplicate per differentiation) ( Fig 1B ). To identify the specific interactions responsible for this effect, QMI data were processed using two independent statistical approaches: (1) adaptive nonparametric test with adjustable alpha cutoff (ANC) was designed specifically for QMI datasets ( Smith 2016 ) and compares bead distributions for each of 400 interactions while correcting for multiple comparisons; while (2) correlation network analysis (CNA) (Horvath and Langfelder 2008) clusters interactions by similar behavior across all experimental samples, and asks if any co-regulated clusters, termed modules , correlate with experimental variables ( Fig 1A ). Only interactions that meet both CNA and ANC significance thresholds are reported hereafter. CNA identified four modules that correlated with genotype, arbitrarily color-coded Blue (correlation coefficient (CC) = 0.86, p = 2 × 10 −5 ), Magenta ( CC = 0.67, p = 0.005), Turquoise (CC=0.57, p = 0.02) and Black CC = 0.56, p = 0.03). Note, however, that Magenta and Black also correlated strongly with a Batch effect (CC = 0.93 p = 3 × 10 −7 and CC = 0.8 p = 2 × 10 −4 , respectively) and Turquoise correlated with a Batch x Cell line effect (CC = 0.94 p = 4 × 10 −7 ). Blue was the only module not confounded by batch and/or cell line. A heatmap of interactions that were ANC-significant for either batch 1 or 2, and a member of the four genotype-significant CNA modules ( Fig 1C ) demonstrates these effects. Interactions that were ANC-significant across both batches are in bold/italic. Immunoprecipitation target GLUR1, probe GLUR1 (abbreviated GLUR1_GLUR1) was the interaction most significantly correlated to the blue module, and was consistently reduced in SORL1 KO cells across both batches ( Fig 1D ). Interactions in which the IP and probe are for the same protein can indicate changes in abundance, multimerization, or changes in other co-associated proteins that may occlude antibody binding sites ( Heavner 2021 ); prior published data showing reduced protein localization of GLUR1 supports the former interpretation (Mishra 2022). GLUR2_GLUR2 was significantly correlated to the black module, and was only significant by ANC for batch 2 ( Fig 1E ). These changes, combined with changes in GLUR1_GLUR2 in batch 2, suggest a shift in AMPA receptor subunit usage due to known endosomal GLUR1 trafficking deficits in SORL1 mutants (Mishra 2022). In addition, alterations in Shank1_panShank ( Fig 1F ) or Homer_GLUR2 ( Fig 1G ) indicate changes to the Homer-Shank scaffolding network that supports AMPA receptor localization. Extensive changes to downstream effector molecules such as the RAS-GTPase SynGAP (exemplified by NMDAR2A_SynGAP, Fig 1H ) or the SRC-family kinase FYN ( Fig 1I ) indicate altered signal transduction downstream of synaptic activity that could affect glutamate receptor properties or synaptic plasticity. A node-edge diagram limited to interactions that were ANC-CNA significant in both batches demonstrates the synapse-wide scope of the changes observed ( Fig 1J ). These data supported further investigations into synaptic activity in SORL1 KO neurons. Loss of SORL1 alters synapse morphology QMI analysis demonstrated changes in multiple categories of excitatory synaptic proteins and their interactions. To determine if these changes altered the number and morphology of synapses we performed an immunocytochemical analysis of pre and post synaptic proteins. Wild-type and SORL1 KO neuronal cultures were matured, fixed on DPP 30, and stained for SYNAPSIN and HOMER1 to identify pre and post synaptic structures, respectively. We performed three-dimensional analysis of pre and post synaptic marker signals. We found clear expression of SYNAPSIN and HOMER1 in the MAP2-positive somatodendritic compartments of wild-type and SORL1 KO neurons ( Fig 2A ). Colocalization of the pre and postsynaptic markers shows that both neuronal genotypes form complete synaptic structures ( Fig 2A , arrows). Several differences were detected in SORL1 KO neurons. Consistent with QMI analysis, we detected increased expression of postsynaptic scaffolding protein HOMER1. SORL1 KO HOMER1 puncta were larger and showed a greater sum fluorescence intensity ( Fig 2B ). Download figure Open in new tab Figure 2: SORL1 KO neurons express enlarged pre and post synaptic densities. A: SORL1 deficient neurons exhibited larger and brighter pre and post synaptic structures. Representative micrographs of DPP 30 wild-type and SORL1 KO neurons, immunostained for pre and post synaptic markers SYNAPSIN (SYN) and HOMER1, and neuronal somatodendritic compartment marker MAP2. Lower panels: Higher magnification images of MAP2 positive dendrites. MAP2 signal borders indicated by grey outline. HOMER1 and SYNAPSIN colocalization highlighted with white arrow. B: Quantification of sum fluorescence intensity and volume of three-dimensional HOMER1 postsynaptic puncta. HOMER1 puncta in SORL1 KO neurons contained greater fluorescence signal and were larger than in isogenic wild-type control neurons. C: Quantification of sum fluorescence intensity and volume of three-dimensional SYNAPSIN (SYN) presynaptic puncta. SYNAPSIN puncta in SORL1 KO neurons contained greater fluorescence signal and were larger than in isogenic wild-type control neurons. D: Quantification of count and volume of three-dimensional puncta of HOMER1 and SYN colocalizations. No difference in the total number of colocalization puncta were detected between wild-type and SORL1 KO neurons, indicating similar levels of synapses. The volumes of colocalization puncta were larger in SORL1 KO neurons, suggesting larger synapses. Number of colocalizations was normalized to the total surface area of MAP2 volumes to control for image to image differences in number of neurons and dendrites. 80,000 to 140,000 HOMER1 puncta were analyzed and 40,000-60,000 SYNAPSIN puncta were analyzed from N = 32-34 images per genotype. Two clones per genotype were analyzed. HOMER1 and SYNAPSIN puncta quantification data points are plotted, with mean value indicated by horizontal dashed line. Colocalization metrics were calculated per cell and represented as violin plots with data median indicated by bolt dashed line and upper and lower quartiles represented by dotted lines. HOMER1 and SYNAPSIN puncta were determined to be non-normal (D’Agostino and Pearson test) analyzed for statistical significance with the Kolmogorov-Smirnov test. Outliers were identified using the ROUT (Q = 1%) method and removed for statistical testing and plotting. Statistical testing for colocalization quantifications were unpaired t-tests. Significance marked as follows: P < 0.01 (**), P < 0.0001 (****). Not significant comparisons are unmarked. Staining of the presynaptic marker SYNAPSIN (SYN) revealed an increased fluorescence intensity and puncta volume in SORL1 KO ( Fig 2C ), an analogous increase in the presynaptic compartment alongside the postsynaptic structure. Three-dimensional colocalization analysis revealed no difference in the number of colocalized HOMER1 and SYNAPSIN positive puncta, indicating that the number of synapses does not differ between SORL1 KO neurons and isogenic controls ( Fig 2D , upper). The volume of HOMER1 and SYNAPSIN colocalizations were larger in SORL1 KO neurons, suggesting larger synapses ( Fig 2D , lower). Taken together, these data indicate in SORL1 KO neuronal synapses, altered synaptic structure is found on both sides of the synapse. We next compared pre and post synaptic protein signal in the somatic and dendritic compartments ( Fig S1 ). The observed increased HOMER1 puncta volume and intensity in the whole-cell analysis was found in both the somatic and dendritic compartments of SORL1 KO neurons ( Fig S1 A,C ). A small but significant decrease in SYNAPSIN puncta volume and intensity were found in the soma of SORL1 deficient neurons ( Fig S1 B ). Conversely, SYNAPSIN puncta in the dendrites of SORL1 KO neurons exhibited greater intensity and volume than isogenic control neurons ( Fig S1 D ), demonstrating that the observed differences in presynaptic SYNAPSIN staining we found in the whole-cell analysis ( Fig 2 ) was primarily driven by differences in the SORL1 KO dendrites. SORL1 KO neurons show clear differences in excitatory synaptic densities, recapitulating the increased HOMER1 interactions identified by QMI ( Fig 1 ) and accompanying changes in the presynaptic compartment. Download figure Open in new tab Figure S1: Abnormal SORL1 KO postsynaptic densities are localized to the dendritic compartment. Quantification of pre and post synaptic SYNAPSIN and HOMER1 puncta in somatic (A,B) and dendritic (C,D) compartments of DPP 30 wild-type and SORL1 KO neurons. A-B: Pre and post synaptic puncta volume and sum puncta fluorescence intensity in neuronal soma. C-D: Pre and post synaptic puncta volume and sum puncta fluorescence intensity in neuronal dendrites. HOMER1 puncta in the soma and dendrites of SORL1 KO neurons were larger and contained greater fluorescence signal. Smaller SYNAPSIN puncta were detected in the soma of SORL1 KO neurons with lower intensity but this difference did not extend into the dendrites, where greater volumes and intensities of presynaptic puncta in SORL1 KO neurons were detected. 20,000 to 25,000 HOMER1 puncta and 12,000 SYNAPSIN puncta were identified and analyzed in somatic compartments. 60,000 to 100,000 HOMER1 puncta and 30,000 to 50,000 SYNAPSIN puncta were identified and analyzed in dendritic compartments. N = 32-34 images per genotype. Two clones per genotype were analyzed. Puncta data points plotted with mean values indicated by horizontal dashed line. Outliers were identified using the ROUT (Q = 1%) method and removed for statistical testing and plotting. Data were tested for normality and were determined to be not normally distributed (D’Agostino and Pearson test). Statistical testing for all quantifications was the Kolmogorov-Smirnov test, with significance marked as follows: P < 0.01 (**), P < 0.0001 (****). Not significant comparisons are unmarked. SORL1 KO neurons have shifted AMPA receptor subunit expression We have shown previously that SORL1 KO neurons have impaired trafficking of the glutamate AMPA receptor subunit GLUR1 (Mishra 2022) and here we find altered interactions of GLUR1 and GLUR2 subunits in SORL1 KO neurons with QMI analysis. We performed an immunocytochemical assay of GLUR1 and GLUR2 expression in matured wild-type and SORL1 KO neurons to further examine the expression profiles of the AMPA receptor subunits. We found GLUR1 and GLUR2 expression in wild-type and SORL1 KO neurons ( Fig 3A ). GLUR1 puncta in wild-type neurons has on average greater sum fluorescence intensities and volumes compared to those in SORL1 KO neurons ( Fig 3B ). Conversely, SORL1 KO neurons had, on average, GLUR2 puncta with greater sum fluorescence intensities and volumes than isogenic wild-type control neurons ( Fig 3C ). These patterns of expression align with GLUR1 and GLUR2 interaction changes observed in QMI ( FIGURE 1 ) and reflect a shift in the balance of GLUR1 and GLUR2 subunit expression profiles when SORL1 dependent trafficking is disrupted. We next measured GLUR1 and GLUR2 puncta in somatic and dendritic compartments and found little difference in comparison to whole-cell analysis ( Fig S2 ), other than GLUR1 puncta volume being similar in the dendrites of wild-type and SORL1 KO neurons. These data show that differences in AMPA receptor subunit expression are occurring cell-wide and not differently in somatic and dendritic compartments. These results add to our previous findings of GLUR1 trafficking being disrupted. Download figure Open in new tab Figure S2: Wild-type and SORL1 KO neuron GLUR1 and GLUR2 levels are consistent across soma and dendrites. Quantification of GLUR1 and GLUR2 immunofluorescent signal captured from matured DPP 30 wild-type and SORL1 KO neuronal somatic and dendritic compartments. A-B: Soma localized GLUR1 and GLUR2 puncta sum intensity and volumes. C-D: Dendrite localized GLUR1 and GLUR2 puncta sum intensity and volumes in wild-type and SORL1 KO neurons. With the exception of GLUR1 puncta volume in dendrites, differences of puncta sum intensity and volume match whole-cell analysis ( Figure 3 ), indicating that changes in GLUR1 and GLUR2 are cell-wide and not localized imbalances of somatic and dendritic levels of the AMPA subunits. N = 10,000-12,000 GLUR1 and GLUR2 puncta were analyzed from somatic and dendritic neuronal structures. 32-34 images per genotype. Two clones per genotype were analyzed. Puncta data points plotted with mean values indicated by horizontal dashed line. Outliers were identified using the ROUT (Q = 1%) method and removed for statistical testing and plotting. Data were tested for normality and were determined to be not normally distributed (D’Agostino and Pearson test). Statistical testing for all quantifications was the Kolmogorov-Smirnov test, with significance marked as follows: P < 0.0001 (****). Not significant comparisons are unmarked Download figure Open in new tab Figure 3: SORL1 deficient neurons exhibit abnormal AMPA subunit GLUR1 and GLUR2 levels A: Representative images of isogenic wild-type and SORL1 KO neurons, immunostained for AMPA receptor subunits GluR1, GluR2, somatodendritic neuronal marker MAP2, and DAPI. B: Quantification of GluR1 puncta sum fluorescence intensity and volume from wild-type and SORL1 KO neurons. GluR1 puncta in SORL1 KO neurons were smaller and had lower sum fluorescence intensity. C: GluR2 puncta sum fluorescence intensity and volumes from wild-type and SORL1 KO neurons. Immunostained GluR2 puncta were brighter and larger in volume in SORL1 KO neurons compared to wild-type neurons. N = 10,000-12,000 GluR1 and GluR2 puncta were analyzed from 32-34 images per genotype. Two clones per genotype were analyzed. ICC puncta data points plotted with mean values indicated by horizontal dashed line. Outliers were identified using the ROUT (Q = 1%) method and removed for statistical testing and plotting. Data were tested for normality and were determined to be not normally distributed (D’Agostino and Pearson test). Statistical testing for all quantifications was the Kolmogorov- SORL1 deficient neurons are hyperexcitable Broad changes in synaptic protein composition suggest physiological consequences of loss of SORL1. Laboratory models of AD repeatedly demonstrate hyperexcitable neuronal phenotypes at the network and cellular level ( Busche 2008 , Ghatak 2019 , Lerdkrai 2018 , Targa Dias Anastacio 2022 ). Here, to assess how SORL1 contributes to neuronal function, we measured the spiking activity of SORL1 KO and isogenic control hiPSC derived neurons on multiple electrode array (MEA) plates for sixty days post differentiation. Over this time period SORL1 KO cultures exhibited several hyperexcitable phenotypes ( Fig 4 ). SORL1 KO neurons generated more action potentials than isogenic controls, with SORL1 KO cultures generating greater mean firing rates ( Fig 4B ). The difference in firing rate was larger in more mature cultures. The timing of KO neuron’s spiking was also less regular, as demonstrated by the higher coefficient of variation of the inter-spike interval in SORL1 KO neuron cultures ( Fig 4C ). Similarly, this increased variability in timing in SORL1 KO neurons was greatest in more mature cultures. Download figure Open in new tab Figure 4: SORL1 KO neurons are hyperexcitable A: Representative spike histograms (upper panel) and raster plots (lower panel) of wild-type and SORL1 KO neuronal cultures plated on multi-electrode arrays (MEAs) at 14 and 57 DPP, before and after the onset of synchronized network bursting. B: Time course of mean firing rates of wild-type and SORL1 KO neuron cultures. C: Time course of the mean coefficient of variation (CV) of inter-spike intervals (ISI) of action potentials generated by wild-type and SORL1 KO neuron cultures. D: Time course of the mean rate of network bursts generated by wild-type and SORL1 KO cultures. E: Time course of the mean coefficient of variation of inter-burst intervals (IBI) of network bursts generated by wild-type and SORL1 KO neuron cultures. SORL1 KO neurons generated more spikes with more variable timing and this pattern was conserved at the network activity level, reflected by the differences between wild-type and SORL1 KO neuron culture network bursting activity. F: Time course of the mean firing rate of spikes generated during a network burst. G: Time course of the mean firing rate of spikes generated outside of a network burst. Neuronal firing rates were higher during SORL1 KO neuron network bursts while no difference was detected in the network burst interstices. N = 54-63 MEA wells analyzed per genotype. Two clones per genotype were analyzed. Data represented as MEAN ± SEM. Two-way ANOVA statistical testing was used for all quantifications. Significance of the interaction of genotype and time marked on the plots as follows: P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****). Unsignificant comparisons are unmarked. After approximately three weeks, KO and control neuronal cultures began to exhibit coordinated activity indicative of neurotransmission-driven network firing, or network bursting, consistent with pre and post synaptic protein staining ( Fig 4A , Fig 2 ). SORL1 KO neuronal hyperexcitability was conserved at the network level and KO cultures generated more network bursts compared to isogenic controls ( Fig 4D ). SORL1 KO culture network bursting also showed more irregularity in the timing of the interburst interval (IBI), reflected by an increased coefficient of variation in the inter-burst interval ( Fig 4E ). The hyperexcitability of SORL1 KO cultures was accelerated by the onset of network bursting, so we examined the rates of spiking inside and outside the network burst window. Surprisingly, we found no difference between genotypes in the mean firing rate outside of coordinated bursting ( Fig 4G ). Within the duration of the burst, however, SORL1 KO neurons fired at an increased rate ( Fig 4F ). These results indicate that SORL1 KO neuron hyperexcitability is driven during coordinated network bursts. Hyperexcitability in SORL1 deficient neurons is glutamate dependent In vitro cultures of hiPSC excitatory neurons developing coordinated network bursting is an established phenomenon ( Heikkila 2009 , Plumbly 2019 ). The hyperactivity of SORL1 KO network activity and changes observed in excitatory synapse protein interactions and morphology implicate changes to synapses could be driving SORL1 KO neuron hyperexcitability. Here, we tested if SORL1 KO hyperexcitability persisted with blockade of fast ionotropic glutamatergic AMPA and NMDA receptors. Following the development of network bursting, wild-type and SORL1 KO neuronal cultures were treated with 10 µM NBQX and 50 µM APV to block AMPA and NMDA mediated neurotransmission. Network bursting was completely abolished in both wild-type and SORL1 KO cultures with treatment ( Fig 5A ). Mean firing rates and coefficient of variation of inter-spike intervals also decreased with antagonist application ( Fig 5B,C ). Notably, the differences in these metrics between wild-type and SORL1 KO neurons were eliminated when glutamate neurotransmission was blocked and restored after antagonist washout. Inhibitory neurotransmission shapes network activity in other models ( Odawara 2016 , Plumbly 2019 , Cobb 1995 ) and dual-SMAD neuronal differentiation can lead to the expression of inhibitory neuron genes and proteins ( Israel 2012 ). We found no significant contribution of GABA A mediated inhibitory neurotransmission in our neural networks. Application of 10 µM gabazine leads to small, non-significant increases in firing rates of wild-type and SORL1 KO neuronal cultures ( Fig 5D , Fig S3 ). No changes to network bursting rate were observed ( Fig S3B ) and the coefficient of variation of the inter-burst interval ( Fig S3C ) with gabazine treatment. Wild-type and SORL1 KO neurons showed similar levels and patterns of spiking when glutamate receptors were blocked, suggesting that the hyperexcitability phenotypes we found are the result of abnormal excitatory glutamate neurotransmission. Download figure Open in new tab Figure S3: GABA A mediated inhibitory neurotransmission does not contribute to SORL1 KO hyperexcitability. A: Representative spike histograms (top panels) and raster plots (bottom panels) of wild-type and SORL1 KO MEA cultures treated with GABA A receptor antagonist Gabazine (10 μM). Little to no change is observed with Gabazine treatment, suggesting that inhibitory neurotransmission is not influencing or shaping patterns of electrical activity in these cultures. B: Network bursting rate of wild-type and SORL1 KO cultures with GABA A blockade. No changes are observed in wild-type or SORL1 KO cultures. C: Coefficient of variation of network burst inter-burst intervals (IBI CV). Two-way ANOVA analysis revealed no significant interaction effect of genotype and Gabazine condition. N = 20-21 wild-type and SORL1 KO cultures were used for Gabazine experiments at approximately DPP 45. Two wild-type clones and one SORL1 KO clone were utilized. Data are represented by mean value ± SEM. Significance values of two-way ANOVA interaction results (cell line X drug condition) were not significant for both network bursting rate and IBI CV. Download figure Open in new tab Figure 5: SORL1 KO neuron hyperexcitability is driven by glutamatergic neurotransmission. A: Representative SORL1 KO neuron culture spike histogram (upper panel) and raster plot (lower panel) before and after treatment with glutamate receptor AMPA and NMDA antagonists NBQX and APV (10 μM and 50 μM respectively). Glutamate receptor blockade eliminates network bursting, indicating that network activity is driven primarily by glutamatergic neurotransmission. B: Mean firing rates of wild-type and SORL1 KO cultures treated with NBQX and APV. C: Coefficient of variation of the inter-spike interval of wild-type and SORL1 KO neuron cultures treated with NBQX and APV. Wild-type and SORL1 KO neurons have significantly different mean firing rates and CVs of ISIs in baseline and washout conditions but are similar when glutamate neurotransmission is blocked, demonstrating that SORL1 hyperactivity is driven by glutamate receptor activity. D: Mean firing rate of wild-type and SORL1 KO neurons treated with the GABA A receptor antagonist Gabazine (10 μM). A small, non-significant increase in firing rate was measured with Gabazine treatment in both wild-type and SORL1 KO neurons, showing that inhibitory GABAergic neurotransmission does not play a role in SORL1 KO hyperactivity. N = 24 wild-type cultures and 36 SORL1 KO cultures were used for NBQX and APV experiments at approximately DPP 45. Two clones per genotype were analyzed for NBQX and APV experiments. N = 27 wild-type and SORL1 KO cultures were used for Gabazine experiments at approximately DPP 45. Two wild-type clones and one SORL1 KO clone were analyzed for Gabazine experiments. Data represented as MEAN ± SEM. Two-way ANOVA statistical testing was performed for all quantifications. P-values of Sidak’s multiple comparisons test between wild-type and SORL1 KO genotypes at baseline, antagonist condition, and washout are represented on the plots with significance values marked as: P ≤ 0.05 (*), P ≤ 0.01 (**). Non-significant comparisons are unmarked. Hyperexcitability in SORL1 KO neurons is not solely due to increases in secreted amyloid beta SORL1 KO and SORL1 loss-of-function variant hiPSC neurons secrete more soluble Aβ as a result of impaired trafficking ( Knupp 2020 , Mishra 2023). Soluble Aβ causes increased neuronal activity and impaired synaptic plasticity in hiPSC neurons and other laboratory models ( Zott 2019 , Ping 2015 , Busche 2008 , Palop 2010 ). To examine if increased Aβ could explain the physiological phenotypes we observe in SORL1 KO neurons, we compared neurophysiological activity of neural cultures using MEA and harvested medium from MEA wells to measure secreted Aβ protein concentrations of SORL1 KO neurons, an isogenic line carrying homozygous amyloid precursor protein (APP) mutations KM670 and 671NL, the APP Swedish mutations (APP Swe+/+ ), and an isogenic wild-type control. APP Swe+/+ neurons secreted much higher levels of Aβ 1-40 and 1-42 than wild-type control and SORL1 KO neurons ( Fig 6A-D ) (Shin and Evitts, 2023). Consistent with prior studies, APP Swe+/+ neurons were hyperactive compared to isogenic wild-type controls ( Fig 6E,F ). APP Swe+/+ neurons successfully developed coordinated network bursting, demonstrating that the increased levels of soluble Aβ does not interfere with network synapse development. Interestingly, mean firing rates of APP Swe+/+ neurons were similar to SORL1 KO neurons prior to the development of network bursting ( Fig 6E ). After network bursting was established, APP Swe+/+ neurons continued to fire more than isogenic controls but significantly less than SORL1 KO neurons ( Fig 6F ). All cell lines generated more Aβ 1-42 after developing bursting and wild-type and SORL1 KO neurons also secreted significantly more Aβ 1-40 ( Fig S4 ). Network bursting SORL1 KO neurons generated significantly more spiking than APP Swe+/+ neurons ( Fig 6F ). Despite the increased level of neuronal spiking observed prior to and after network bursting in APP Swe+/+ neurons compared with control neurons, APP Swe+/+ neurons did not generate more network bursts compared with WT neuronal cultures, in contrast with SORL1 KO cultures, which maintained their hyperactivity at the network level ( Fig 6G ). These results emphasize the SORL1 KO driven alterations to excitatory synapse composition being the principal driver of SORL1 KO hyperexcitability. When firing rates and concentrations of secreted Aβ are compared directly, we found that the hyperexcitability observed in SORL1 KO neuronal cultures is not due solely to increased Aβ secretion ( Fig 6H ). Download figure Open in new tab Figure S4: Amyloid beta secretion increases with neuronal network bursting A: Concentrations (pg/ml) of secreted Aβ 1-40 harvested from wild-type, SORL1 KO, and APP Swe neurons before and after the onset of network bursting (DPP 14 and DPP 35, respectively). Wild-type and SORL1 KO neurons exhibited a significant increase in Aβ 1-40 secretion after the onset of synchronized network bursting. APP Swe neurons change in Aβ 1-40 trended upwards as well. B: Concentrations of (pg/ml) of secreted Aβ 1-42 harvested from wild-type, SORL1 KO, and APP Swe neurons before and after the establishment of synchronized network bursting (DPP 14 and DPP 35, respectively). All three cell lines secreted significantly more Aβ 1-42 following network bursting onset. N = 4 wild-type and SORL1 KO cultures. N = 2 APP Swe +/+ cultures. 2 clones of wild-type and SORL1 KO cell lines were used, 1 clone of APP Swe +/+ was used. Each point represents the average of two technical replicates measured from conditioned medium collected from one MEA well ± SD. Paired t-tests results are indicated as follows: P ≤ 0.05 (*) and P ≤ 0.01 (**). Not significant comparisons are not marked. Download figure Open in new tab Figure 6: SORL1 KO neuron hyperexcitability is independent of secreted amyloid beta. A-B: Concentrations of secreted Aβ 1-40 and Aβ 1-42 measured from conditioned medium collected from DPP 14 MEA cultures of wild-type, SORL1 KO, and APP Swe +/+ neurons, before network bursting has been established. C-D: Concentrations of secreted Aβ 1-40 and Aβ 1-42 peptide measured from conditioned medium collected from DPP 35 MEA cultures of wild-type, SORL1 KO, and APP Swe +/+ neurons, after network bursting has been begun. At both timepoints, prebursting (DPP 14) and postbursting (DPP 30), APP Swe +/+ neurons secrete significantly greater levels of Aβ 1-40 and Aβ 1-42 than isogenic wild-type controls and SORL1 KO neurons. N = 4 wild-type and SORL1 KO cultures. N = 2 APP Swe +/+ cultures. 2 clones of wild-type and SORL1 KO cell lines were used, 1 clone of APP Swe +/+ was used. Each point represents the average of two technical replicates measured from conditioned medium collected from one MEA well ± SD. One-way ANOVAs were used for statistical testing. Tukey’s multiple comparisons test results are represented on the plot with significance marked: E-F: Mean firing rates or wild-type, SORL1 KO, and APP Swe +/+ neurons at conditioned medium collection timepoints ,DPP 14 and DPP 30. SORL1 KO and APP Swe +/+ neurons exhibited increased firing rates compared to isogenic wild-type controls in prebursting conditions. After the establishment of network bursting, SORL1 KO neurons generated action potentials at a greater rate than wild-type and APP Swe +/+ neurons. G: Network bursting rates of wild-type, SORL1 KO, and APP Swe +/+ at DPP 30. SORL1 KO cultures generated network bursts at a greater rate than both wild-type and APP Swe +/+ cultures, demonstrating that network hyperactivity is not solely dependent on secreted levels of Aβ 1-40 or Aβ 1-42 . N = wild-type (24), SORL1 KO (24), and APP Swe +/+ (20). Data represented as violin plots with data median indicated by bolt dashed line and upper and lower quartiles represented by dotted lines. One-way ANOVAs were used for statistical testing. Tukey’s multiple comparisons test results are represented on the plot with significance marked: H: Scatter plot of secreted Aβ 1-40 peptide concentration vs mean firing rate for both prebursting and postbursting timepoints. Despite the greatly increased levels of Aβ 1-40 and Aβ 1-42 peptides present in the medium, the mean firing rate and network bursting rate of APP Swe +/+ cultures fail to recapitulate the levels of hyperexcitability observed in SORL1 KO neurons. Data points represent the mean values of each genotype’s Aβ 1-40 peptide concentration and mean firing rate ± SEM. Significance indicated by: P ≤ 0.05 (*), P ≤ 0.01 (**), P ≤ 0.001 (***), and P ≤ 0.0001 (****). Not significant comparisons are not marked. Loss of SORL1 inhibits long-term potentiation induction Long-term potentiation (LTP) is a form of synaptic plasticity in which GLUR1-containing AMPA receptors are rapidly trafficked to the postsynaptic membrane in response to high levels of presynaptic activity ( Malenka 2004 ). Inhibited LTP has been linked to AD in several models, and the SORLA binding partner retromer is a trafficking complex that is necessary for LTP ( Temkin 2017 , Simoes 2021 ) Chemical LTP (cLTP) protocols are effective approaches to inducing similar changes in neurons, and are especially useful in vitro where networks lack organized afferent inputs. The cAMP second messenger pathway can be positively modulated to induced LTP in hiPSC neuronal cultures. Network bursting is dependent upon glutamate receptor activity ( Fig.5 ) and induction of cAMP cLTP results in increased levels of network bursting in hiPSC derived dopaminergic neuron networks (Pre 2022). Here, we utilized a cLTP protocol developed in Pre et al 2022 to examine the network activity of SORL1 KO neuronal cultures that have undergone LTP induction. The adenylyl cyclase activator forskolin was used in combination with the phosphodiesterase inhibitor rolipram to stimulate the cAMP second messenger pathway and prevent cAMP degradation. We induced cLTP in wild-type and SORL1 KO neurons plated on MEA culture plates and assayed the electrical activity for 72 hours post induction. Wild-type neurons displayed increases in network bursting rate that lasted up to 72 hours post induction ( Fig 7A,B ). In contrast, SORL1 KO neurons failed to recover network bursting activity for up to 24 hours post induction ( Fig 7 A,B ), with significantly lower fold change bursting rates in comparison with wild-type cultures. Mean firing rates measured from MEA cultures increased in wild-type cultures slower than the increase in network bursting, similar to what has been described in dopaminergic neuronal network cLTP responses (Pre 2022). cLTP treated SORL1 KO neurons showed no change in mean firing rates compared to pretreatment baseline conditions ( Fig 7C ). Interestingly, SORL1 KO neurons do exhibit an increased network bursting rate once network activity has resumed and the differences between wild-type and SORL1 KO network bursting fades at the 48 hour timepoint post induction ( Fig 7A,B ). These data demonstrate that SORL1 is an important component of activity driven increases of excitatory synaptic strength. Download figure Open in new tab Figure 7: SORL1 KO neurons exhibit inhibited long-term potentiation. A: Representative spike histograms (left panels) and raster plots (right panels) of wild-type and SORL1 KO cultures across the time course of cLTP treatment and recovery. Raster plots represent twenty seconds of the full recording shown by the spike histogram, highlighting the pattern of activity that constitutes a network burst. Wild-type and SORL1 KO neurons display a breakdown of synaptically-driven activity 30 minutes following chemical LTP (cLTP) induction utilizing forskolin and rolipram. B: Time course of fold change in network bursting rate, normalized to baseline values, of wild-type and SORL1 KO neurons following cLTP induction. Network bursting in wild-type cultures resumed in wild-type cultures within four hours of cLTP induction but SORL1 KO neurons failed to recover by this time. Network bursting in SORL1 KO neurons is reestablished on average by 24 hours, albeit at a smaller relative rate compared with wild-type neurons. No change was observed with control vehicle treatment C: Time course of fold change in mean firing rate, normalized to baseline values, of wild-type and SORL1 KO neurons following cLTP induction. SORL1 KO neurons did not exhibit increased firing rates following cLTP treatment, in contrast with wild-type neurons. N = 9-11 MEA cultures of wild-type and SORL1 KO neurons. 2 clones of each genotype were used for cLTP experiments. Two-way ANOVA and Tukey’s multiple comparisons statistical tests were performed. Significance between wild-type cLTP and SORL1 KO cLTP groups are marked on B and C plots with P ≤ 0.05 (*), P ≤ 0.01 (**), P ≤ 0.001 (***), and P ≤ 0.0001 (****). Non-significant differences are not marked. Discussion In this study, we report that loss of SORL1 expression affects excitatory synapse composition, morphology, and neuronal function, including the impairment of long-term potentiation. Synaptic dysfunction is a very early event in AD progression ( Pelucchi 2022 ) and the main contributor to cognitive decline ( Terry 1991 ), thus preventing this impairment at an early stage would be a beneficial therapeutic option for AD. The relationship between neuronal hyperexcitability with synaptic function and dysfunction is complex. In AD, hyperexcitability has been attributed to increases in Aβ molecules which can directly depolarize neurons and disrupt inhibitory circuits, and pTau pathology which can impair axonal transport and destabilize neuronal networks ( Pelucchi 2022 ). However, impaired endosomal recycling has emerged as a hypothesis that is upstream of both Aβ and pTau, and can contribute to both of these pathologies (Small and Petsko 2015). Multiple proteins necessary for normal synaptic composition are trafficked through the endosomal network ( Park 2004 , Dong 2024 ). Based on our previous work (Mishra 2022), we hypothesized that impaired endosomal recycling in SORL1 deficient neurons would alter synaptic protein trafficking. Using an unbiased approach, QMI, we show that multiple synaptic proteins, including receptors and scaffolding proteins, show altered protein-protein interactions. HOMER1 and PSD95 postsynaptic scaffolding proteins both demonstrate increased interactions in SORL1 KO neurons, suggesting post synaptic density alterations. This is corroborated by our immunostaining results showing larger volumes and intensities of HOMER1 signal ( Fig 2 ). We also observed increased fluorescence intensity and volume of SYNAPSIN puncta. While these increases could explain the hyperexcitability we observe, combined with reduced long-term potentiation, this could represent an ultimately maladaptive response to altered synaptic protein trafficking by which the neuron attempts to maintain synaptic strength by increasing synaptic size and expanding the molecular machinery for synaptic transmission, such as the accumulation of scaffolding proteins. Interestingly, we found similar numbers of synapses in wild-type and SORL1 KO neurons ( Fig 2 ), showing that SORL1 trafficking contributes to the maintenance of proteins at synapses but does not result in compensatory formation of new synapses. Similarly, the hyperactivity of SORL1 deficient neurons does not cause a homeostatic compensatory decrease in synapses to normalize neuronal firing levels. We also observe alterations in AMPA receptor subunits GLUR1 and GLUR2 both by QMI and by immunostaining. There is a significant decrease in GLUR1-GLUR1 interactions in SORL1 KO neurons, which corroborates our previous findings of reduced GLUR1 subunits trafficked to the neuronal membrane. Loss of GLUR1 on neuronal membranes has also been seen in models of retromer deficiency, suggesting that SORLA-retromer trafficking is a key mechanism for maintaining function AMPA receptors. We observe increased interactions between GLUR2 and increased intensity in GLUR2 immunostaining. While GLUR2 is also trafficked via retromer (Tian 2015), its interaction with SORLA is not clear. In a mouse neuronal model of VPS35 deficiency, decreased synaptic transmission could be rescued by GLUR2 overexpression (Tian 2015). Thus, increased GLUR2 levels in our model may be a compensatory effect. GLUR2 subunit incorporation into AMPA receptors prolongs the decay time of synaptic currents, and leads to more efficient EPSP-AP coupling (Savtchouk 2011), which could contribute to the increased bursting we observed. In addition, GLUR2 subunits control the calcium permeability of AMPA receptors and block calcium influx after post transcriptional Q/R site editing. Decreased GLUR2 editing in the neurodegenerative disease amyotrophic lateral sclerosis (ALS) contributes to excitotoxicity in motor neurons ( Kwak 2010 ). Similar mechanisms may be occurring when SORLA mediated transport is disrupted and shifts AMPA receptor subunit levels, as we have demonstrated here. Blocking glutamate neurotransmission and synchronized network bursting normalizes SORL1 KO neuron hyperexcitability demonstrating that aberrant firing rates are driven by glutamate neurotransmission. Wild-type and SORL1 KO neuron firing rates are similar outside of synchronized network bursts, further support for a functional consequence of the resulting excitatory synaptic protein shifts we observed. Larger synapses are correlated with stronger synaptic currents ( Hazan 2020 , Holler 2021 ) and we observe larger HOMER1-SYNAPSIN colocalization volumes which could contribute to the hyperactivity observed in our cells. Changes in excitatory-inhibitory balance has been implicated in AD-driven hyperactivity but we find no effect of blocking GABA A inhibitory receptors ( Figs 5 , S3) beyond a small, non-significant increase in firing rate in both wild-type and SORL1 KO neurons, making impaired inhibition a less likely cause in this model. In our previous study (Mishra 2022), bulk RNA sequencing showed increases in ion channel expression, which may be contributing to hyperexcitability independently of changes at the synapse. The hyperexcitability phenotypes we find here are consistent with Raska, Satkova, and Plesingrova et al (co-submitted with this manuscript). There, the authors find hyperactivity in not only SORL1 KO neurons but also the missense variant p.Y1816C. These results build upon other reports of detrimental effects of SORL1 pathogenic variants (Mishra 2023, Fazeli 2024-1, Fazeli 2024-2, Rovelet-Lecrux 2021 ) and demonstrate that neuronal function is dependent upon functional SORLA protein and not affected solely by its complete loss of protein expression. Additionally, their measurements are taken from a different parental cell line and generated utilizing a transcription factor induced differentiation protocol, distinct from the dual-SMAD inhibition protocol used here, emphasizing that the critical component in our studies to be missing or malfunctioning SORL1 . Together, these results demonstrate the robustness of SORL1 ’s importance for healthy neuronal function and contributions to early disease pathology. Our findings demonstrate that SORL1 deficiency disrupts synaptic homeostasis through a mechanism that is largely independent of Aβ. While increases in Aβ due to loss of SORL1 are documented ( Knupp 2020 ) and may contribute to some of the hyperexcitability we observe, neurons with the APP Swedish mutation secrete more than 5X the amount of Aβ and are less hyperactive after the onset of synchronized, synaptically driven network activity. This suggests that the primary driver of neuronal hyperexcitability in SORL1 deficient neurons is due to altered synapse composition as a result of SORLA-dependent endosomal trafficking and recycling. Synaptic plasticity is dependent on the movement of receptors and proteins at the synapse, and deficits in long-term potentiation have been demonstrated in AD models and brains ( Walsh 2002 , Di Lorenzo 2017) and SORLA’s binding partner retromer ( Temkin 2017 , Simoes 2021 ). Here, we report the novel finding that loss of SORL1 inhibits the ability of hiPSC neurons to induce LTP in response to chemical stimulation. Recycling endosomes near the post synaptic structure hold and shuttle AMPA receptors for LTP induction ( Park 2004 ) and SORL1 deficient neurons demonstrate recycling endosome swelling and increased co-localization with GLUR1 AMPA subunits (Mishra 2022). The lowered levels of response to cLTP induction we see here are likely due to a failure in the movement of GLUR1-containing AMPA receptors in response to induction. Interestingly, we do see a slow response to cLTP induction in SORL1 KO neurons, showing that synaptic plasticity isn’t completely lost, reflecting low levels of recycling endosome to post synaptic membrane trafficking or diffusion of extrasynaptic AMPA receptors. Conclusions Our work supports a model where synaptic trafficking defects represent an early and potentially targetable mechanism in AD pathogenesis. These insights suggest that therapeutic interventions aimed at restoring proper endosomal function or correcting synaptic protein trafficking may offer effective strategies for preventing cognitive decline in AD. Declarations Ethics approval Not applicable. Consent for publication Not applicable. Availability of data and materials All data and material in the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This work is supported by NIH R01AG080585 and a Cure Alzheimer’s Fund grant to J.E.Y. C.A.W. was supported by the UW Alzheimer’s Disease Training Program T32AG52354 Author’s contributions Conceptualization-C.A.W., J.E.Y. Methodology and Analysis: C.A.W, S.E.R, V.S, S.E.P.S. Writing-C.A.W., J.E.Y., Revising and Editing-S.E.R, S.E.P.S. Funding Acquisition-J.E.Y., C.A.W. Acknowledgements We thank Drs. Olav Andersen, Scott Small and Greg Petsko for helpful discussions. We thank the members of the Young Laboratory and the UW Alzheimer’s research community for critical feedback and discussions on this work. Funder Information Declared National Institute on Aging, https://ror.org/049v75w11 , R01AG080585 , T32AG52354 Cure Alzheimer’s Fund, https://ror.org/05ewr7t48 List of abbreviations Aβ Amyloid beta AD Alzheimer’s disease ANC Adaptive nonparametric test with adjustable alpha cutoff APOE Apolipoprotein E APP Amyloid precursor protein BDNF Brain-derived neurotrophic factor cLTP Chemical long-term potentiation CNA Correlation network analysis DPP Days post plating ELN Endo-lysosomal network GDNF Glial derived neurotrophic factor GLUR1 Glutamate receptor 1 GLUR2 Glutamate receptor 2 GWAS Genome-wide association study hiPSC Human induced pluripotent stem cell KO Knock-out MEA Multi-electrode array pTau hyper-phosphorylated tau protein QMI Quantitative multiplex immunoprecipitation SORL1 Sortilin-related receptor 1 (gene) SORLA Sortilin-related receptor with A-type repeats (protein) References ↵ Barthelson , K. , Newman , M. , & Lardelli , M . ( 2020 ). Sorting Out the Role of the Sortilin-Related Receptor 1 in Alzheimer’s Disease . Journal of Alzheimer’s Disease Reports , 4 ( 1 ), 123 . doi: 10.3233/ADR-200177 OpenUrl CrossRef PubMed ↵ Bellenguez , C. , Charbonnier , C. , Grenier-Boley , B. , Quenez , O. , le Guennec , K. , Nicolas , G. , Chauhan , G. , Wallon , D. , Rousseau , S. , Richard , A. C. , Boland , A. , Bourque , G. , Munter , H. M. , Olaso , R. , Meyer , V. , Rollin-Sillaire , A. , Pasquier , F. , Letenneur , L. , Redon , R. , … Jurici , S. ( 2017 ). Contribution to Alzheimer’s disease risk of rare variants in TREM2, SORL1, and ABCA7 in 1779 cases and 1273 controls . Neurobiology of Aging , 59 , 220.e1-220.e9. doi: 10.1016/J.NEUROBIOLAGING.2017.07.001 OpenUrl CrossRef ↵ Brown , E. A. , Neier , S. C. , Neuhauser , C. , Schrum , A. G. , & Smith , S. E. P . ( 2019 ). Quantitative Multiplex co-Immunoprecipitation: Measuring input-specific changes in protein interaction networks at mesoscale . Journal of Visualized Experiments : JoVE , 2019 ( 150 ), 10.3791/60029. doi: 10.3791/60029 OpenUrl CrossRef ↵ Busche , M. A. , Eichhoff , G. , Adelsberger , H. , Abramowski , D. , Wiederhold , K. H. , Haass , C. , Staufenbiel , M. , Konnerth , A. , & Garaschuk , O . ( 2008 ). Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer’s disease . Science , 321 ( 5896 ), 1686 – 1689 . doi: 10.1126/SCIENCE.1162844/SUPPL_FILE/BUSCHE.SOM.PDF OpenUrl Abstract / FREE Full Text ↵ Cobb , S. R. , Buhl , E. H. , Halasy , K. , Paulsen , O. , & Somogyi , P . ( 1995 ). Synchronization of neuronal activity in hippocampus by individual GABAergic interneurons . Nature , 378 ( 6552 ), 75 – 78 . doi: 10.1038/378075A0;KWRD=SCIENCE OpenUrl CrossRef PubMed Web of Science di Lorenzo , F. , Ponzo , V. , Bonnì , S. , Motta , C. , Negrão Serra , P. C. , Bozzali , M. , Caltagirone , C. , Martorana , A. , & Koch , G. ( 2016 ). Long-term potentiation–like cortical plasticity is disrupted in Alzheimer’s disease patients independently from age of onset . Annals of Neurology , 80 ( 2 ), 202 – 210 . doi: 10.1002/ANA.24695;WEBSITE:WEBSITE:PERICLES;WGROUP:STRING:PUBLICATION OpenUrl CrossRef PubMed ↵ Dong , J. , Tong , W. , Liu , M. , Liu , M. , Liu , J. , Jin , X. , Chen , J. , Jia , H. , Gao , M. , Wei , M. , Duan , Y. , & Zhong , X . ( 2024 ). Endosomal traffic disorders: a driving force behind neurodegenerative diseases . Translational Neurodegeneration , 13 ( 1 ). doi: 10.1186/S40035-024-00460-7 , OpenUrl CrossRef Fazeli , E. , Child , D. D. , Bucks , S. A. , Stovarsky , M. , Edwards , G. , Rose , S. E. , Yu , C. E. , Latimer , C. , Kitago , Y. , Bird , T. , Jayadev , S. , Andersen , O. M. , & Young , J. E . ( 2024 ). A familial missense variant in the Alzheimer’s disease gene SORL1 impairs its maturation and endosomal sorting . Acta Neuropathologica , 147 ( 1 ), 1 – 18 . doi: 10.1007/S00401-023-02670-1/FIGURES/7 OpenUrl CrossRef Fazeli , E. , Fazeli , E. , Fojtík , P. , Holstege , H. , & Andersen , O. M . ( 2024 ). Functional characterization of SORL1 variants in cell-based assays to investigate variant pathogenicity . Philosophical Transactions of the Royal Society B , 379 ( 1899 ). doi: 10.1098/RSTB.2022.0377 OpenUrl CrossRef ↵ Ghatak , S. , Dolatabadi , N. , Trudler , D. , Zhang , X. , Wu , Y. , Mohata , M. , Ambasudhan , R. , Talantova , M. , & Lipton , S. A . ( 2019 ). Mechanisms of hyperexcitability in alzheimer’s disease hiPSC-derived neurons and cerebral organoids vs. Isogenic control . ELife , 8 . doi: 10.7554/ELIFE.50333 OpenUrl CrossRef ↵ Griffiths , J. , & Grant , S. G. N . ( 2023 ). Synapse pathology in Alzheimer’s disease . Seminars in Cell & Developmental Biology , 139 , 13 – 23 . doi: 10.1016/J.SEMCDB.2022.05.028 OpenUrl CrossRef PubMed ↵ Guo , L. , Tian , J. , & Du , H . ( 2017 ). Mitochondrial Dysfunction and Synaptic Transmission Failure in Alzheimer’s Disease . Journal of Alzheimer’s Disease , 57 ( 4 ), 1071 – 1086 . doi: 10.3233/JAD-160702/ASSET/0269B2D7-0469-404A-8EA0-5C78E82AFF69/ASSETS/GRAPHIC/10.3233_JAD-160702-FIG1.JPG OpenUrl CrossRef ↵ Hazan , L. , & Ziv , N. E . ( 2020 ). Activity Dependent and Independent Determinants of Synaptic Size Diversity . Journal of Neuroscience , 40 ( 14 ), 2828 – 2848 . doi: 10.1523/JNEUROSCI.2181-19.2020 OpenUrl Abstract / FREE Full Text ↵ Heavner , W. E. , Lautz , J. D. , Speed , H. E. , Gniffke , E. P. , Immendorf , K. B. , Welsh , J. P. , Baertsch , N. A. , & Smith , S. E. P . ( 2021 ). Remodeling of the Homer-Shank interactome may mediate homeostatic plasticity . Science Signaling , 14 ( 681 ), eabd7325 . doi: 10.1126/SCISIGNAL.ABD7325 OpenUrl Abstract / FREE Full Text ↵ Heikkilä , T. J. , Ylä-Outinen , L. , Tanskanen , J. M. A. , Lappalainen , R. S. , Skottman , H. , Suuronen , R. , Mikkonen , J. E. , Hyttinen , J. A. K. , & Narkilahti , S . ( 2009 ). Human embryonic stem cell-derived neuronal cells form spontaneously active neuronal networks in vitro . Experimental Neurology , 218 ( 1 ), 109 – 116 . doi: 10.1016/J.EXPNEUROL.2009.04.011 OpenUrl CrossRef PubMed ↵ Holler , S. , Köstinger , G. , Martin , K. A. C. , Schuhknecht , G. F. P. , & Stratford , K. J . ( 2021 ). Structure and function of a neocortical synapse . Nature 2021 591:7848 , 591 ( 7848 ), 111 – 116 . doi: 10.1038/s41586-020-03134-2 OpenUrl CrossRef PubMed ↵ Holstege , H. , van der Lee , S. J. , Hulsman , M. , Wong , T. H. , van Rooij , J. G. J. , Weiss , M. , Louwersheimer , E. , Wolters , F. J. , Amin , N. , Uitterlinden , A. G. , Hofman , A. , Ikram , M. A. , van Swieten , J. C. , Meijers-Heijboer , H. , van der Flier , W. M. , Reinders , M. J. T. , van Duijn , C. M. , & Scheltens , P. ( 2017 ). Characterization of pathogenic SORL1 genetic variants for association with Alzheimer’s disease: A clinical interpretation strategy . European Journal of Human Genetics , 25 ( 8 ), 973 – 981 . doi: 10.1038/EJHG.2017.87;TECHMETA=43,45;SUBJMETA=1283,132,2056,2421,308,375,53,692,699;KWRD=ALZHEIMER OpenUrl CrossRef PubMed ↵ Israel , M. A. , Yuan , S. H. , Bardy , C. , Reyna , S. M. , Mu , Y. , Herrera , C. , Hefferan , M. P. , van Gorp , S. , Nazor , K. L. , Boscolo , F. S. , Carson , C. T. , Laurent , L. C. , Marsala , M. , Gage , F. H. , Remes , A. M. , Koo , E. H. , & Goldstein , L. S. B. ( 2012 ). Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells . Nature 2012 482:7384 , 482 ( 7384 ), 216 – 220 . doi: 10.1038/nature10821 OpenUrl CrossRef PubMed Web of Science ↵ John , A. , & Reddy , P. H . ( 2021 ). Synaptic basis of Alzheimer’s disease: Focus on synaptic amyloid beta, P-tau and mitochondria . Ageing Research Reviews , 65 . doi: 10.1016/j.arr.2020.101208 OpenUrl CrossRef PubMed ↵ Katsu-Jiménez , Y. , Alves , R. M. P. , & Giménez-Cassina , A . ( 2017 ). Food for thought: Impact of metabolism on neuronal excitability . Experimental Cell Research , 360 ( 1 ), 41 – 46 . doi: 10.1016/J.YEXCR.2017.03.002 OpenUrl CrossRef PubMed ↵ Kim , J. P. , Jung , S. H. , Jang , B. , Cho , M. , Song , M. , Kim , J. , Kim , B. , Lee , H. , Shin , D. , Lee , E. H. , Jang , H. , Kim , B. H. , Ham , H. , Kim , D. , Raj , T. , Cruchaga , C. , Kim , H. J. , Na , D. L. , Seo , S. W. , & Won , H. H . ( 2025 ). Cross-ancestry genome-wide association study identifies implications of SORL1 in cerebral beta-amyloid deposition . Nature Communications , 16 ( 1 ), 3150 . doi: 10.1038/S41467-025-57751-4 OpenUrl CrossRef PubMed ↵ Knupp , A. , Mishra , S. , Martinez , R. , Braggin , J. E. , Szabo , M. , Kinoshita , C. , Hailey , D. W. , Small , S. A. , Jayadev , S. , & Young , J. E . ( 2020 ). Depletion of the AD Risk Gene SORL1 Selectively Impairs Neuronal Endosomal Traffic Independent of Amyloidogenic APP Processing . Cell Reports , 31 ( 9 ), 107719 . doi: 10.1016/j.celrep.2020.107719 OpenUrl CrossRef PubMed ↵ Kwak , S. , Hideyama , T. , Yamashita , T. , & Aizawa , H . ( 2010 ). AMPA receptor-mediated neuronal death in sporadic ALS . Neuropathology , 30 ( 2 ), 182 – 188 . doi: 10.1111/J.1440-1789.2009.01090.X OpenUrl CrossRef PubMed ↵ Lambert , J. C. , Ibrahim-Verbaas , C. A. , Harold , D. , Naj , A. C. , Sims , R. , Bellenguez , C. , Jun , G. , DeStefano , A. L. , Bis , J. C. , Beecham , G. W. , Grenier-Boley , B. , Russo , G. , Thornton-Wells , T. A. , Jones , N. , Smith , A. v. , Chouraki , V. , Thomas , C. , Ikram , M. A. , Zelenika , D. , … Seshadri , S. ( 2013 ). Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease . Nature Genetics , 45 ( 12 ), 1452 . doi: 10.1038/NG.2802 OpenUrl CrossRef PubMed ↵ Langfelder , P. , & Horvath , S . ( 2008 ). WGCNA: An R package for weighted correlation network analysis . BMC Bioinformatics , 9 ( 1 ), 1 – 13 . doi: 10.1186/1471-2105-9-559/FIGURES/4 OpenUrl CrossRef PubMed ↵ Lautz , J. D. , Brown , E. A. , Williams VanSchoiack , A. A. , & Smith , S. E. P . ( 2018 ). Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network . Journal of Neurochemistry , 146 ( 5 ), 540 . doi: 10.1111/JNC.14466 OpenUrl CrossRef PubMed Lautz , J. D. , Tsegay , K. B. , Zhu , Z. , Gniffke , E. P. , Welsh , J. P. , & Smith , S. E. P . ( 2021 ). Synaptic protein interaction networks encode experience by assuming stimulus-specific and brain-region-specific states . Cell Reports , 37 ( 9 ), 110076 . doi: 10.1016/J.CELREP.2021.110076 OpenUrl CrossRef PubMed Leek , J. T. , Johnson , W. E. , Parker , H. S. , Jaffe , A. E. , & Storey , J. D . ( 2012 ). The sva package for removing batch effects and other unwanted variation in high-throughput experiments . Bioinformatics , 28 ( 6 ), 882 – 883 . doi: 10.1093/BIOINFORMATICS/BTS034 OpenUrl CrossRef PubMed Web of Science ↵ Lerdkrai , C. , Asavapanumas , N. , Brawek , B. , Kovalchuk , Y. , Mojtahedi , N. , del Moral , M. O. , & Garaschuk , O . ( 2018 ). Intracellular Ca2+ stores control in vivo neuronal hyperactivity in a mouse model of Alzheimer’s disease . Proceedings of the National Academy of Sciences of the United States of America , 115 ( 6 ), E1279 – E1288 . doi: 10.1073/PNAS.1714409115/SUPPL_FILE/PNAS.201714409SI.PDF OpenUrl Abstract / FREE Full Text ↵ Levy , S. , Sutton , G. , Ng , P. C. , Feuk , L. , Halpern , A. L. , Walenz , B. P. , Axelrod , N. , Huang , J. , Kirkness , E. F. , Denisov , G. , Lin , Y. , MacDonald , J. R. , Pang , A. W. C. , Shago , M. , Stockwell , T. B. , Tsiamouri , A. , Bafna , V. , Bansal , V. , Kravitz , S. A. , … Venter , J. C . ( 2007 ). The Diploid Genome Sequence of an Individual Human . PLOS Biology , 5 ( 10 ), e254 . doi: 10.1371/JOURNAL.PBIO.0050254 OpenUrl CrossRef PubMed ↵ Malenka , R. C. , & Bear , M. F . ( 2004 ). LTP and LTD: An Embarrassment of Riches . Neuron , 44 ( 1 ), 5 – 21 . doi: 10.1016/J.NEURON.2004.09.012 OpenUrl CrossRef PubMed Web of Science Mishra , S. , Knupp , A. , Kinoshita , C. , Williams , C. A. , Rose , S. E. , Martinez , R. , Theofilas , P. , & Young , J. E . ( 2023 ). Pharmacologic enhancement of retromer rescues endosomal pathology induced by defects in the Alzheimer’s gene SORL1 . Stem Cell Reports , 18 ( 12 ), 2434 – 2450 . doi: 10.1016/J.STEMCR.2023.10.011 OpenUrl CrossRef PubMed ↵ Mishra , S. , Knupp , A. , Szabo , M. P. , Williams , C. A. , Kinoshita , C. , Hailey , D. W. , Wang , Y. , Andersen , O. M. , & Young , J. E . ( 2022 ). The Alzheimer’s gene SORL1 is a regulator of endosomal traffic and recycling in human neurons . Cellular and Molecular Life Sciences 2022 79:3 , 79 ( 3 ), 1 – 22 . doi: 10.1007/S00018-022-04182-9 OpenUrl CrossRef ↵ Mishra , S. , Morshed , N. , Sidhu , S. B. , Kinoshita , C. , Stevens , B. , Jayadev , S. , & Young , J. E . ( 2025 ). The Alzheimer’s Disease Gene SORL1 Regulates Lysosome Function in Human Microglia . GLIA , 73 ( 7 ), 1329 – 1348 . doi: 10.1002/GLIA.70009;WGROUP:STRING:PUBLICATION OpenUrl CrossRef PubMed ↵ Odawara , A. , Katoh , H. , Matsuda , N. , & Suzuki , I . ( 2016 ). Physiological maturation and drug responses of human induced pluripotent stem cell-derived cortical neuronal networks in long-term culture . Scientific Reports , 6 ( 1 ), 1 – 14 . doi: 10.1038/srep26181 OpenUrl CrossRef PubMed ↵ Palop , J. J. , & Mucke , L . ( 2010 ). Amyloid-Β-induced neuronal dysfunction in Alzheimer’s disease: From synapses toward neural networks . Nature Neuroscience , 13 ( 7 ), 812 – 818 . doi: 10.1038/NN.2583;SUBJMETA=1283,1643,365,375,378,548,631,692,699,92;KWRD=ALZHEIMER OpenUrl CrossRef PubMed Web of Science ↵ Park , M. , Penick , E. C. , Edwards , J. G. , Kauer , J. A. , & Ehlers , M. D . ( 2004 ). Recycling endosomes supply AMPA receptors for LTP . Science , 305 ( 5692 ), 1972 – 1975 . doi: 10.1126/SCIENCE.1102026/SUPPL_FILE/PARK.SOM.PDF OpenUrl Abstract / FREE Full Text ↵ Pelucchi , S. , Gardoni , F. , di Luca , M. , & Marcello , E. ( 2022 ). Synaptic dysfunction in early phases of Alzheimer’s Disease . Handbook of Clinical Neurology , 184 , 417 – 438 . doi: 10.1016/B978-0-12-819410-2.00022-9 OpenUrl CrossRef PubMed ↵ Ping , Y. , Hahm , E. T. , Waro , G. , Song , Q. , Vo-Ba , D. A. , Licursi , A. , Bao , H. , Ganoe , L. , Finch , K. , & Tsunoda , S . ( 2015 ). Linking Aβ42-Induced Hyperexcitability to Neurodegeneration, Learning and Motor Deficits, and a Shorter Lifespan in an Alzheimer’s Model . PLOS Genetics , 11 ( 3 ), e1005025 . doi: 10.1371/JOURNAL.PGEN.1005025 OpenUrl CrossRef ↵ Plumbly , W. , Brandon , N. , Deeb , T. Z. , Hall , J. , & Harwood , A. J . ( 2019 ). L-type voltage-gated calcium channel regulation of in vitro human cortical neuronal networks . Scientific Reports , 9 ( 1 ), 1 – 12 . doi: 10.1038/s41598-019-50226-9 OpenUrl CrossRef PubMed ↵ Pré , D. , Wooten , A. T. , Biesmans , S. , Hinckley , S. , Zhou , H. , Sherman , S. P. , Kakad , P. , Gearhart , J. , & Bang , A. G . ( 2022 ). Development of a platform to investigate long-term potentiation in human iPSC-derived neuronal networks . Stem Cell Reports , 17 ( 9 ), 2141 – 2155 . doi: 10.1016/J.STEMCR.2022.07.012 OpenUrl CrossRef PubMed ↵ Rose , S. E. , Frankowski , H. , Knupp , A. , Berry , B. J. , Martinez , R. , Dinh , S. Q. , Bruner , L. T. , Willis , S. L. , Crane , P. K. , Larson , E. B. , Grabowski , T. , Darvas , M. , Dirk Keene , C. , & Young , J. E. ( 2018 ). Leptomeninges-Derived Induced Pluripotent Stem Cells and Directly Converted Neurons From Autopsy Cases With Varying Neuropathologic Backgrounds . Journal of Neuropathology & Experimental Neurology , 77 ( 5 ), 353 – 360 . doi: 10.1093/JNEN/NLY013 OpenUrl CrossRef PubMed ↵ Rovelet-Lecrux , A. , Feuillette , S. , Miguel , L. , Schramm , C. , Pernet , S. , Quenez , O. , Ségalas-Milazzo , I. , Guilhaudis , L. , Rousseau , S. , Riou , G. , Frébourg , T. , Campion , D. , Nicolas , G. , & Lecourtois , M . ( 2021 ). Impaired SorLA maturation and trafficking as a new mechanism for SORL1 missense variants in Alzheimer disease . Acta Neuropathologica Communications , 9 ( 1 ), 1 – 22 . doi: 10.1186/S40478-021-01294-4/FIGURES/10 OpenUrl CrossRef PubMed Savtchouk , I. , & Liu , S. J . ( 2011a ). Remodeling of synaptic AMPA receptor subtype alters the probability and pattern of action potential firing . Journal of Neuroscience , 31 ( 2 ), 501 – 511 . doi: 10.1523/JNEUROSCI.2608-10.2011 , OpenUrl Abstract / FREE Full Text Savtchouk , I. , & Liu , S. J . ( 2011b ). Remodeling of synaptic AMPA receptor subtype alters the probability and pattern of action potential firing . Journal of Neuroscience , 31 ( 2 ), 501 – 511 . doi: 10.1523/JNEUROSCI.2608-10.2011 , OpenUrl Abstract / FREE Full Text ↵ Shin , Y. J. , Evitts , K. M. , Jin , S. , Howard , C. , Sharp-Milgrom , M. , Schwarze-Taufiq , T. , Kinoshita , C. , Young , J. E. , & Zheng , Y . ( 2023 ). Amyloid beta peptides (Aβ) from Alzheimer’s disease neuronal secretome induce endothelial activation in a human cerebral microvessel model . Neurobiology of Disease , 181 , 106125 . doi: 10.1016/J.NBD.2023.106125 OpenUrl CrossRef PubMed ↵ Simoes , S. , Guo , J. , Buitrago , L. , Qureshi , Y. H. , Feng , X. , Kothiya , M. , Cortes , E. , Patel , V. , Kannan , S. , Kim , Y. H. , Chang , K. T. , Hussaini , S. A. , Moreno , H. , di Paolo , G. , Andersen , O. M. , & Small , S. A. ( 2021 ). Alzheimer’s vulnerable brain region relies on a distinct retromer core dedicated to endosomal recycling . Cell Reports , 37 ( 13 ), 110182 . doi: 10.1016/J.CELREP.2021.110182 OpenUrl CrossRef PubMed Small , S. A. , & Petsko , G. A . ( 2020 ). Endosomal recycling reconciles the Alzheimer’s disease paradox . Science Translational Medicine , 12 ( 572 ), 1717 . doi: 10.1126/SCITRANSLMED.ABB1717 , OpenUrl CrossRef ↵ Smith , S. E. P. , Neier , S. C. , Reed , B. K. , Davis , T. R. , Sinnwell , J. P. , Eckel-Passow , J. E. , Sciallis , G. F. , Wieland , C. N. , Torgerson , R. R. , Gil , D. , Neuhauser , C. , & Schrum , A. G . ( 2016 ). Multiplex matrix network analysis of protein complexes in the human TCR signalosome . Science Signaling , 9 ( 439 ), rs7. doi: 10.1126/SCISIGNAL.AAD7279 OpenUrl CrossRef ↵ Targa Dias Anastacio , H. , Matosin , N. , & Ooi , L. ( 2022 ). Neuronal hyperexcitability in Alzheimer’s disease: what are the drivers behind this aberrant phenotype? Translational Psychiatry 2022 12:1 , 12 ( 1 ), 1 – 14 . doi: 10.1038/s41398-022-02024-7 OpenUrl CrossRef PubMed ↵ Temkin , P. , Morishita , W. , Goswami , D. , Arendt , K. , Chen , L. , & Malenka , R . ( 2017 ). The Retromer Supports AMPA Receptor Trafficking During LTP . Neuron , 94 ( 1 ), 74 – 82 .e5. doi: 10.1016/j.neuron.2017.03.020 OpenUrl CrossRef PubMed ↵ Terry , R. D. , Masliah , E. , Salmon , D. P. , Butters , N. , DeTeresa , R. , Hill , R. , Hansen , L. A. , & Katzman , R . ( 1991 ). Physical basis of cognitive alterations in alzheimer’s disease: Synapse loss is the major correlate of cognitive impairment . Annals of Neurology , 30 ( 4 ), 572 – 580 . doi: 10.1002/ANA.410300410;REQUESTEDJOURNAL:JOURNAL:15318249 OpenUrl CrossRef PubMed Web of Science ↵ Tzioras , M. , McGeachan , R. I. , Durrant , C. S. , & Spires-Jones , T. L . ( 2023 ). Synaptic degeneration in Alzheimer disease . Nature Reviews Neurology , 19 ( 1 ), 19 – 38 . doi: 10.1038/S41582-022-00749-Z , OpenUrl CrossRef PubMed ↵ Vossel , K. A. , Tartaglia , M. C. , Nygaard , H. B. , Zeman , A. Z. , & Miller , B. L . ( 2017 ). Epileptic activity in Alzheimer’s disease: causes and clinical relevance . The Lancet Neurology , 16 ( 4 ), 311 – 322 . doi: 10.1016/S1474-4422(17)30044-3 OpenUrl CrossRef PubMed ↵ Walsh , D. M. , Klyubin , I. , Fadeeva , J. v. , Cullen , W. K. , Anwyl , R. , Wolfe , M. S. , Rowan , M. J. , & Selkoe , D. J. ( 2002 ). Naturally secreted oligomers of amyloid β protein potently inhibit hippocampal long-term potentiation in vivo . Nature , 416 ( 6880 ), 535 – 539 . doi: 10.1038/416535A;KWRD=SCIENCE OpenUrl CrossRef PubMed Web of Science ↵ Young , J. E. , Boulanger-Weill , J. , Williams , D. A. , Woodruff , G. , Buen , F. , Revilla , A. C. , Herrera , C. , Israel , M. A. , Yuan , S. H. , Edland , S. D. , & Goldstein , L. S. B . ( 2015 ). Elucidating Molecular Phenotypes Caused by the SORL1 Alzheimer’s Disease Genetic Risk Factor Using Human Induced Pluripotent Stem Cells . Cell Stem Cell , 16 ( 4 ), 373 – 385 . doi: 10.1016/J.STEM.2015.02.004 OpenUrl CrossRef PubMed ↵ Young , J. E. , Fong , L. K. , Frankowski , H. , Petsko , G. A. , Small , S. A. , & Goldstein , L. S. B . ( 2018 ). Stabilizing the Retromer Complex in a Human Stem Cell Model of Alzheimer’s Disease Reduces TAU Phosphorylation Independently of Amyloid Precursor Protein . Stem Cell Reports , 10 ( 3 ), 1046 – 1058 . doi: 10.1016/j.stemcr.2018.01.031 OpenUrl CrossRef PubMed ↵ Yuan , S. H. , Martin , J. , Elia , J. , Flippin , J. , Paramban , R. I. , Hefferan , M. P. , Vidal , J. G. , Mu , Y. , Killian , R. L. , Israel , M. A. , Emre , N. , Marsala , S. , Marsala , M. , Gage , F. H. , Goldstein , L. S. B. , & Carson , C. T . ( 2011 ). Cell-Surface Marker Signatures for the Isolation of Neural Stem Cells, Glia and Neurons Derived from Human Pluripotent Stem Cells . PLOS ONE , 6 ( 3 ), e17540 . doi: 10.1371/JOURNAL.PONE.0017540 OpenUrl CrossRef PubMed ↵ Zott , B. , Simon , M. M. , Hong , W. , Unger , F. , Chen-Engerer , H. J. , Frosch , M. P. , Sakmann , B. , Walsh , D. M. , & Konnerth , A . ( 2019 ). A vicious cycle of β amyloid−dependent neuronal hyperactivation . Science , 365 ( 6453 ), 559 – 565 . doi: 10.1126/SCIENCE.AAY0198/SUPPL_FILE/AAY0198_ZOTT_SM.PDF OpenUrl Abstract / FREE Full Text View the discussion thread. Back to top Previous Next Posted July 29, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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