{"paper_id":"031813a2-2e97-4764-9d94-d1d9bd223765","body_text":"=== R E V I E W   C O M M O N S   M A N U S C R I P T ===\nIMPORTANT:\nManuscripts subm itted to Review Com m ons are peer reviewed in a journal-agnostic way.\nUpon transfer of the peer reviewed preprint to a journal, the referee reports will be available in full to the handling editor.\nThe identity of the referees will NOT be com m unicated to the authors unless the reviewers choose to sign their report.\nThe identity of the referee will be confidentially disclosed to any affiliate journals to which the m anuscript is transferred.\nGUIDELINES:\nFor reviewers: https://www.reviewcom m ons.org/reviewers\nFor authors: https://www.reviewcom m ons.org/authors\nCONTACT:\nThe Review Com m ons office can be contacted directly at: office@reviewcom m ons.org\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n1 \n \nLipid droplet dynamics during the budding yeast 1 \ncell cycle influence the timing of cell cycle START 2 \nHanna M. Terpstra1, Katerina Loukogiannaki1, Vasiliki Tsousi1, Matthias Heinemann1* 3 \n1Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of 4 \nGroningen, Nijenborgh 7, 9747 AG Groningen, Netherlands 5 \n 6 \n*Corresponding authors, m.heinemann@rug.nl 7 \nABSTRACT 8 \nRecent work has revealed that metabolism is dynamic over the budding yeast cell cycle , showing that 9 \nthe NAD(P)H autofluorescence oscillates , and protein and lipid biosynthesis are dynamic. Dynamic 10 \nstorage and liquidation of neutral lipids over the cell cycle could contribute to the se metabolic 11 \ndynamics. However, the dynamics of neutral lipids over the cell cycle are as yet unknown. To elucidate 12 \nthem, we established mNeonGreen-Tgl3 and Pln1-mNeonGreen as protein markers for lipid droplets 13 \n(LDs) and determined LD dynamics during the cell cycle with single-cell time-lapse microscopy . We 14 \nfound oscillations in the LD number over the cell cycle, with a notable trough around START. Deletion  of 15 \nthe genes responsible for either the synthesis of triacylglycerol (TAG) or its mobilisation from LDs 16 \nlowered LD numbers and abolished the oscillation in LD number. Moreover, in these deletion mutants, 17 \nwe found START to be delayed , suggesting that the mobilisation of TAG from LDs is required for its 18 \ntimely occurrence. The influence of LD dynamics on the timing of START emphasises that research 19 \nstudying cell cycle commitment should consider storage lipid metabolism as a potential contributor to 20 \ncell cycle START. 21 \n 22 \nINTRODUCTION 23 \nProper control of cell growth and division is fundamental for cells. In eukaryotes, the cyclin/cyclin-24 \ndependent kinase (CDK) machinery is considered the primary regulator of this process (Mendenhall & 25 \nHodge, 1998). However, recent work in S. cerevisiae suggests that an intrinsically dynamic metabolism 26 \ncould also play a role in cell cycle regulation. For instance, CDK-independent oscillations in NAD(P)H 27 \nlevels were found essential for cell division (Papagiannakis et al., 2017) a nd an increase in protein 28 \nbiosynthesis during the G1 phase was found to drive commitment to the cell cycle by temporarily 29 \nincreasing Cln3 levels (Litsios et al., 2019) . As the dynamics of metabolism evidently are also involved in 30 \ncell cycle control, it is important to precisely understand the metabolic dynamics and the ir underlying 31 \ncause in order to investigate the precise mechanisms through which metabolism controls cell cycle 32 \nprogression.  33 \nMultiple metabolic processes oscillate along the cell cycle: the glycolytic flux peaks between cytokinesis 34 \nand late G1 phase (Monteiro et al., 2019) and mobilisation of stored carbohydrates occurs during G1 35 \n(Müller et al., 2003; Silljé et al., 1997; Zhao et al., 2016). Lipid biosynthesis peaks during S/G2/M 36 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n2 \n \n(Takhaveev et al., 2023), coinciding with increased protein levels of the biosynthetic enzymes for both 37 \nergosterol (Blank et al., 2020) and fatty acids (Blank et al., 2017) and increased triacylglycerol levels 38 \n(Blank et al., 2020) in this phase. 39 \nBesides lipid synthesis, lipid storage could also be dynamic during the cell cycle. Neutral lipids, namely 40 \ntriacylglycerols (TAG) and steryl esters (SE), are stored in lipid droplets (LDs), which are critical during 41 \ngrowth transitions. When budding yeast enters stationary phase, TAGs are synthesised and stored in LDs 42 \n(Czabany et al., 2008; Markgraf et al., 2014). In contrast, when yeast cells exit stationary phase, these 43 \nneutral lipids are mobilised and LD size and number decrease (Ganesan et al., 2020; Markgraf et al., 44 \n2014; Ouahoud et al., 2018). Furthermore, mutants deficient in TAG mobilisation resume growth more 45 \nslowly than the wild type when cells grown to stationary phase are shifted into fresh medium (Kurat et 46 \nal., 2009; Ouahoud et al., 2018) . Based on these findings, we wondered whether neutral lipid storage 47 \ncould also be dynamic during the cell cycle and if so, whether these dynamics would affect cell cycle 48 \nprogression.  49 \nIn this study, we established protein markers for LDs and used th em to uncover cell cycle oscillations of 50 \nthe number of LDs. These oscillations show a trough around cell cycle START and a peak midway through 51 \nS/G2/M, indicating that LDs are first depleted and then re-formed as the cell cycle progresses. In strains 52 \nlacking enzymes for TAG synthesis or mobilisation, the dynamics of the LD number are lost and START is 53 \ndelayed. Our findings indicate that mobilisation of TAG from LDs contributes to the timely occurrence of 54 \nSTART, suggesting that LD dynamics play a role in cell cycle control.  55 \nRESULTS 56 \nEstablishing protein markers for dynamic, dye-free identification of lipid droplets 57 \nTo elucidate the dynamic behaviour of lipid droplets during the cell cycle in S. cerevisiae , we used 58 \nfluorescence microscopy in combination with a microfluidic setup (Huberts et al., 2013; Lee et al., 2012), 59 \nwhich allows the generation of dynamic single-cell data over several cell cycles under constant growth 60 \nconditions. To visualise LDs, we did not use chemical stains, since this would have required the 61 \ncontinuous addition of a dye to the growth medium to counteract the dilution of the dye as cells grow. 62 \nInstead, to achieve dye-free identification of LDs, we chose to tag proteins of the LD proteome (Grillitsch 63 \net al., 2011) that localise to LDs and no other organelles  (SGD, 2017), with mNeonGreen and use these 64 \ntagged proteins as reporters for LDs. Using image databases of S. cerevisiae with GFP-tagged proteins 65 \n(Huh et al., 2003; Weill et al., 2018), we selected proteins of the LD proteome whose LD localisation 66 \nremained unchanged after introduction of a fluorescent tag. Specifically, we selected Pln1, Tgl3 and Rrt8 67 \nas candidate protein markers for LDs. Pln1 stimulates the accumulation of TAG and stabilises LDs (Gao et 68 \nal., 2017). Tgl3 is a TAG lipase and thus mobilis es neutral lipids from LDs (Athenstaedt & Daum, 2003; 69 \nChauhan et al., 2015) and Rrt8 has been implicated in spore wall assembly (Lin et al., 2013) and 70 \ntransport of plasma membrane proteins (Ueno et al., 2016). 71 \nTo verify that these candidate reporter proteins indeed localise to LDs and can thus serve as LD markers , 72 \nwe fixed cells with formaldehyde, stained LDs with the red fluorescent dye BODIPY- TR and then 73 \nassessed colocalisation between the stained LDs and the mNeonGreen-tagged marker proteins. Given 74 \nthat the chosen marker proteins have different LD-related functions, we considered that certain marker 75 \nproteins might localise only to a subset of all LDs, or reversely, could have a localised function beyond 76 \nLDs.  77 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n3 \n \nBefore testing the colocalisation between LDs detected with BODIPY-TR  and the marker proteins , we 78 \nfirst ensured optimal alignment between images recorded in the green and red fluorescence channels 79 \nusing a bilinear transformation approach. As LDs have an average diameter of 0.4 µm (Czabany et al., 80 \n2008), corresponding to less than three pixels in our microscopy setup , even minor misalignment 81 \nbetween the two fluorescence channels would have significantly distorted the colocalisation analysis. 82 \nWe then used wild-type cells stained with BODIPY-TR to show that the red BODIPY-TR signal is not 83 \ndetected in the GFP channel (Figure S1A-D) . Furthermore, we compared the fluorescence intensity 84 \nmeasured in the GFP channel between a wild-type control, i.e. autofluorescence, and the three LD 85 \nreporter strains and verified that bright spots of autofluorescence occasionally present in GFP images of 86 \nwild-type cells are not detectable in the LD reporter strains with the applied spot detection algorithm 87 \n(Terpstra et al., 2024) and detection settings (Figure S1E-G) . The following colocalisation analysis 88 \ntherefore is not confounded by the detection of GFP puncta that are due to autofluorescence instead of 89 \nan LD marker protein, or artefacts of BODIPY-TR fluorescence detected in the GFP channel.  90 \nNext, to perform the actual colocalisation analysis, we imaged cells stained with BODIPY-TR that also 91 \nexpressed one of the LD reporter fusion proteins Pln1-mNG, mNG-Tgl3 and Rrt8-mNG. We detected 92 \npuncta of the mNeonGreen-tagged LD marker proteins in the GFP channel and LDs stained with BODIPY-93 \nTR in the RFP channel and saw that puncta of the reporter proteins and puncta identified with BODIPY-94 \nTR were often found at similar locations within the cell (Figure 1A). To quantify colocalisation between 95 \npuncta of BODIPY-TR and puncta of the LD marker protein candidates, we differentiat ed between 96 \n‘certain’ and ‘ambiguous’ colocalisation. Colocalisation was classified as ‘certain’ if puncta in the tw o 97 \nimaging channels had identical midpoints or were located at most one pixel apart; ‘ambiguous’ 98 \ncolocalisation refers to puncta whose midpoints were at most two pixels apart. In the following, we 99 \nconsider both the ‘certain’ and ‘ambiguous’ categories as colocalised. 100 \nWe first assessed the fraction of BODIPY-TR puncta that colocalise with a punctum of an LD reporter 101 \nprotein. We found that 82%, 57% and 41% of BODIPY-TR puncta have a corresponding punctum of Pln1-102 \nmNG, Rrt8-mNG and mNG-Tgl3 , respectively (Figure 1B) . We also determined the fraction of reporter 103 \nprotein puncta that colocalise with a BODIPY-TR  punctum and found that 60%, 59% and 54% of protein 104 \npuncta colocalise with a punctum of a stained LD for the marker proteins Rrt8-mNG, Pln1-mNG and 105 \nmNG-Tgl3 (Figure 1B). These data show that not all detected LDs stained with BODIPY-TR  are also visible 106 \nas a marker protein punctum , consistent with the existence of subpopulations of LDs with distinct 107 \nproteomes (Eisenberg-Bord et al., 2018) and lipid content (Meyers et al., 2016). Furthermore, the data 108 \nshow that not all puncta of t he LD reporter proteins colocalise with an LD stained with BODIPY-TR . 109 \nProtein puncta of Pln1 without a corresponding BODIPY-TR puncta could be LDs that are still too small 110 \nfor detection with BODIPY-TR , consistent with the finding that appearance of Pln1 puncta often 111 \nprecedes BODIPY-TR puncta by a few minutes (Gao et al., 2017). The Rrt8-mNG and mNG-Tgl3 puncta 112 \nwithout a corresponding BODIPY-TR punctum might be indicative of alternative functions of these 113 \nproteins, outside LDs. 114 \nOverall, our results show that LDs are heterogeneous, with different marker proteins likely representing 115 \ndistinct subsets of LDs. It is conceivable that one reporter could miss a subset of LDs that another 116 \nreporter could identify . As all three tested LD markers are endogenous proteins with their own 117 \nbiological functions, it is also possible that their dynamics in part reflect other biological functions, 118 \nbesides reporting the dynamics of LDs. Finally, as the localisation of Pln1-mNG is most comparable to 119 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n4 \n \nthat of the LD dye BODIPY-TR, Pln1-mNG is the most suitable LD marker to investigate whether there are 120 \nLD dynamics during the cell cycle. 121 \n 122 \n 123 \nFigure 1. Establishing protein markers for dynamic, dye-free identification of lipid droplets.  (A) Cells expressing a candidate LD 124 \nreporter protein tagged with mNeonGreen (Pln1-mNG, mNG-Tgl3 or Rrt8-mNG) were fixed with formaldehyde, stained with 125 \nBODIPY-TR to visualise LDs and then imaged (upper row). Puncta of marker proteins and LDs stained with BODIPY-TR were 126 \ndetected with a spot detection algorithm (lower row); (B) Colocalisation between LDs stained with BODIPY-TR and puncta of the 127 \nthree candidate LD reporter proteins Pln1-mNG, mNG-Tgl3 and Rrt8- mNG. The left circle of each Venn diagram represents the 128 \nmarker protein puncta identified in the GFP channel image and the right circle represents puncta of BODIPY- TR in the RFP 129 \nchannel image. The orange overlapping region between the two circles represents the colocalised puncta. The white regions 130 \nrepresent puncta whose colocalisation is ambiguous: while puncta in the two fluorescence channels have midpoints close 131 \ntogether, their colocalisation is likely, but not certain. The following number of cells and puncta were assessed in the 132 \ncolocalisation analysis: Pln1-mNG: 153 cells, 304 BODIPY-TR puncta, 424 reporter protein puncta; mNG-Tgl3: 217 cells, 345 133 \nBODIPY-TR puncta, 263 reporter protein puncta; Rrt8-mNG: 245 cells, 323 BODIPY-TR puncta, 302 reporter protein puncta. 134 \n 135 \nNumber of lipid droplets oscillates over the cell cycle 136 \nNext, we performed time-lapse microscopy experiments to determine the number and size of LDs over 137 \nthe cell cycle using Pln1-mNG and mNG-Tgl3 as markers for LDs. Notably, we did not employ Rrt8-mNG 138 \nas an LD reporter in our time-lapse experiments. Due to the low er expression of Rrt8 compared to Pln1 139 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n5 \n \nand Tgl3 (Breker et al., 2013; Chong et al., 2015; SGD, 2017), imaging required higher light exposure, 140 \nresulting in prolonged cell cycle durations (Figure S2) , likely due to phototoxicity. By studying LD 141 \ndynamics with two distinct protein reporters, we aimed to elucidate the more general cell cycle 142 \ndynamics of LDs, independent of specific functions of the reporter proteins . Therefore, we focused on 143 \nglobal similarities in any eventual dynamics observed with the two markers, disregard ing small 144 \ndifferences between the two reporters.   145 \nWe used an automated spot detector algorithm (Terpstra et al., 2024) to detect LDs and to estimate 146 \ntheir size in the time-lapse images. We normalis ed the number of LDs detected within a cell to the area 147 \nof its cross-section. To project the detected LD characteristics on a common cell cycle progression 148 \ncoordinate, we aligned all LD cell cycle trajectories for mitotic exit, START and budding. Finally, we 149 \napplied Gaussian process regression to predict the average cell cycle dynamics of the LD number 150 \nnormalised to the cell cross-area, and of the LD size.  151 \nWith both LD reporter proteins , we found that t he area-normalised LD number oscillates over the cell 152 \ncycle with a minimum around START and a maximum between budding and mitotic exit (Figure 2A-B). 153 \nDensity plots showing all data points reveal the same cell cycle dynamics (Figure S3A-B)  as the 154 \npopulation average dynamics predicted with Gaussian process regression. With Pln1-mNG a second, less 155 \npronounced trough is visible late in the second half of the cell cycle (Figure 2A). In contrast to the area-156 \nnormalised LD number, LD size is constant throughout the cell cycle (Figure 2C-D). Again, density plots 157 \nconfirm the dynamics predicted with Gaussian process regression (Figure S3C-D) . The cell cycle 158 \ndynamics of the summed sizes of all LDs per cell, which notably is not normalised to the cell cross-area, 159 \nstrongly resemble the dynamics of the area-normalised LD number (Figure S3E-F) . The similarity 160 \nbetween these dynamics indicates that the oscillation of the LD number is not an artefact resulting from 161 \nthe normalisation to the cell cross-area. 162 \nWe also noticed that the area-normalised LD number determined with mNG-Tgl3 is almost twofold 163 \nlower than th at measured with Pln1- mNG (Figure 2A- B). This difference could be explained by the 164 \nlocalisation of mNG-Tgl3 to a specific subset of LDs. First, when LD formation in a mutant strain is 165 \ninduced by expression of the diacylglycerol acyltransferase Dga1 , Tgl3 is not detected on newly formed 166 \nLDs for the first hour after induction (Gao et al., 2017) . Second, since Tgl3 is an enzyme that uses TAG as 167 \nits substrate (Athenstaedt & Daum, 2003; Rajakumari & Daum, 2010) and subpopulations of TAG-168 \nspecific LDs have been reported in mammalian cells (Hsieh et al., 2012) and S. pombe  (Meyers et al., 169 \n2016), it is plausible that Tgl3 would localise only to TAG-enriched LDs, while Pln1 does not show this 170 \nspecific localisation.  171 \nTo investigate the similarities and differences of the oscillations observed with the two LD reporters, we 172 \naveraged the three replicates performed with each reporter , normalised the result to its mean and 173 \nplotted the normalised trajectories obtained with Pln1-mNG and mNG-Tgl3 against each other. Here, we 174 \nfound a positive correlation from shortly before START until the final 15% of the cell cycle (Figure 2E), 175 \nindicating that the two markers report comparable dynamics of the area-normalised LD number during 176 \nthis time interval. In contrast, the dynamics are different around mitotic exit. This difference  could arise 177 \nfrom the different functions of the two LD reporter proteins. Specifically, since Pln1 is important for the 178 \nstabilisation of nascent LDs (Gao et al., 2017), the increase in area-normalised LD number observed with 179 \nPln1-mNG at the end of the cell cycle could indicate the formation of new LDs . An increase in LD 180 \nnumbers at the end of the cell cycle would be consistent with the above-average lipid biosynthetic 181 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n6 \n \nactivity at the end of the cell cycle after its peak in the middle of S/G2/M (Takhaveev et al., 2023) and 182 \nthe storage of triacylglycerol during septation (P. L. Yang et al., 2016). If neutral lipids are mobilised from 183 \nthese same LDs after mitotic exit, leading to their disappearance, this would explain the more 184 \npronounced drop in area-normalised LD number observed with Pln1-mNG compared to mNG-Tgl3. 185 \nOverall, employing two distinct LD reporter proteins, we have elucidated the general cell cycle dynamics 186 \nof LDs, which suggest that neutral lipids are mobilised from LDs between mitotic exit and START, as 187 \nevidenced by a trough in the area-normalised LD number, while the neutral lipid stores are replenished 188 \nduring S/G2/M. These  findings demonstrate that LDs are not stagnant organelles containing energy 189 \nreserves to be used in case of nutrient shortages. Instead, their content is mobilised and re-synthesised 190 \nas cells go through the cell cycle. 191 \n 192 \n 193 \nFigure 2.  Number of lipid droplets oscillates over the cell cycle while LD size is constant.  LDs were identified in time-lapse 194 \nmicroscopy images of cells expressing either Pln1- mNG (A, C) or mNG-Tgl3 (B , D) as an LD marker. For each LD reporter strain, 195 \nthree biological replicates, whose results are represented by different line styles, were performed. Cell cycle trajectories were 196 \naligned from one mitotic exit to the next (red vertical lines at cell cycle progression values 0 and 1)  and for occurrence of START 197 \n(bright green vertical line) and budding (orange vertical line) between the se mitotic exit events. Gaussian process regression 198 \nwas used to predict population averages of (A-B) area-normalised number of detected LDs and (C-D) size of the detected LDs; 199 \n(E) To compare the dynamics of area-normalised number of LDs as observed with the reporter proteins Pln1-mNG and mNG-200 \nTgl3, we normalised every Gaussian process regression result to its own average value and plotted results obtained with mNG-201 \nTgl3 against those obtained with Pln1-mNG. The grey line indicates y = x. Thin coloured lines denote the combination of 202 \nindividual replicates using the two LD reporters (3x3 combinations) . The thick black line indicates combined results of the three 203 \nreplicate experiments performed with each LD reporter and was  obtained by averaging the three Gaussian process regression 204 \noutputs for every timepoint. The circular markers on the black curve denote the occurrence of mitotic exit, START and budding. 205 \n 206 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n7 \n \nDynamic LD fluorescence intensity likely results from LD number dynamics 207 \nAfter we discovered the cell cycle dynamics of the LD number, we asked whether the fluorescence 208 \nintensity measured on those LDs was also dynamic. Studying the dynamics of the fluorescence intensity 209 \nof the LD reporters Pln1-mNG and mNG-Tgl3 on LDs and in the cytoplasm could help us distinguish 210 \nbetween observations that reflect the general dynamics of LDs along the cell cycle and observations that 211 \nrelate to the distinct biological functions of the LD marker proteins. We assessed the cell cycle dynamics 212 \nof the fluorescence intensity measured inside the whole cell mask, on LDs and in the cytoplasm. The 213 \nlatter was determined by excluding all pixels belonging to an LD and calculating the average intensity of 214 \nall other pixels within the cell mask. While the fluorescence intensity inside the whole cell and in the 215 \ncytoplasm was found to be almost constant, the fluorescence intensity on LDs oscillates along the cell 216 \ncycle (Figure 3A-B) . With both Pln1-mNG and mNG-Tgl3, the fluorescence intensity on LDs peak ed 217 \naround START and was minimal during S/G2/M.  218 \nTo compare the dynamics of LD fluorescence measured with the two reporters and to distinguish 219 \nbetween general and reporter protein-specific behaviour, we normalised the three replicates performed 220 \nwith each marker protein to their respective means, took the average of the three replicates and plotted 221 \nthe resulting trajectory for mNG-Tgl3 against that for Pln1-mNG . We found that the trajectories 222 \nobtained with the two LD marker proteins correlate positively (Figure 3C) , demonstrating that, along 223 \nmost of the cell cycle, the oscillations of the fluorescence intensities  measured with the two reporters 224 \nare comparable. However, this correlation is absent during the last stretch of the cell cycle, when the 225 \nfluorescence of mNG-Tgl3 on LDs is almost constant, while the fluorescence of Pln1-mNG on LDs is still 226 \ndecreasing. The sharp peak of mNG-Tgl3 intensity on LDs around START could be related to its function 227 \nas a triacylglycerol lipase, as the decrease in the area-normalised LD number between mitotic exit and 228 \nSTART (Figure 2A-B) indeed indicates that neutral lipids are mobilised from LDs before START. 229 \nComparing the dynamics of the area-normalised LD number (Figure 2A-B)  and the  LD fluorescence 230 \nintensity (Figure 3A-B), we noticed that the minimum of the area-normalised LD number around START 231 \ncoincides with the maximum of the LD fluorescence intensity while, vice versa, the maximum of the 232 \narea-normalised LD number and the minimum in the fluorescence intensity on LDs both occur during 233 \nS/G2/M. Indeed, when we plotted the area-normalised LD number against the LD fluorescence, each cell 234 \ncycle trajectory normalised to its own mean, we saw an anticorrelation between these two 235 \ncharacteristics. This anticorrelation was observed both with Pln1-mNG and with mNG-Tgl3 as an LD 236 \nreporter protein (Figure 3D-E). 237 \nTo comprehend why the fluorescence intensity of LDs would anticorrelate with the LD number, we 238 \naimed to unite all observed LD characteristics, i.e. number, size and fluorescence intensity as well as the 239 \nconcentration of the fluorescent reporter protein, in a unified explanation. First, we excluded that a 240 \ndynamic concentration of LD marker protein causes the cell cycle dynamics of the LD fluorescence 241 \nintensity. The concentration, proxied by the average fluorescence intensity within the cell mask, is 242 \nalmost constant for both Pln1-mNG and mNG-Tgl3 (Figure 3A-B) and thus does not elicit the dynamic LD 243 \nfluorescence intensity. Second, changes in partitioning of the LD reporter proteins between LDs and the 244 \ncytoplasm cannot drive the changes in LD fluorescence either. If this partitioning changed, fluorescence 245 \non LDs and in the cytoplasmic would show opposite trends, since marker protein moving from 246 \ncytoplasm to LDs would cause the cytoplasmic fluorescence intensity to decrease and the fluorescence 247 \nintensity of LDs to increase, and vice versa. However, the fluorescence intensity on LDs is dynamic, while 248 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n8 \n \nthe fluorescence in the cytoplasm is constant (Figure 3A-B) . Third, we considered that LD size could 249 \naccount for changes in the fluorescence intensity of LDs. If the amount of marker protein localised to an 250 \nLD stay ed constant while that LD increased in size, the marker protein would be dispersed and the 251 \nfluorescence intensity would decrease. Conversely, if the LD shrunk, the marker protein would become 252 \nmore concentrated, increasing the fluorescence intensity. However, LD size is constant (Figure 2C-D), so 253 \nchanges in LD size do not cause the dynamic fluorescence intensity of LDs. Lastly, the dynamic area-254 \nnormalised LD number (Figure 2A-B)  and its anticorrelation with the fluorescence intensity of LDs 255 \n(Figure 3D-E) can drive the changes in the LD fluorescence intensity. When the number of LDs is low, the 256 \ntotal pool of reporter protein is spread across few LDs, resulting in high fluorescence intensities on those 257 \nLDs. When the number of LDs increases, the same reporter protein molecules are distributed over more 258 \nLDs, and consequently, the fluorescence intensity on the LDs will be lower. Thus, the observed changes 259 \nin LD number could be responsible for the dynamics in LD fluorescence intensity , whereby the 260 \nanticorrelation between fluorescence intensity of LDs and the area-normalised LD number would be 261 \nexplained.  262 \nOverall, our results show that the fluorescence intensity of LDs oscillates along the cell cycle and 263 \nanticorrelates with the area-normalised number of LDs. In contrast, reporter protein concentrations, the 264 \npartitioning of reporter proteins between LDs and the cytoplasm, and LD size are constant. Together, 265 \nthese findings indicate that the cell cycle dynamics of the LD fluorescence intensity are likely driven by 266 \nthe oscillating LD number.  267 \n 268 \n 269 \nFigure 3. Dynamic LD fluorescence intensity anticorrelates with area-normalised LD number.  LDs were identified in time-lapse 270 \nmicroscopy images of cells expressing either Pln1-mNG (A, D) or mNG-Tgl3 (B, E) as an LD reporter protein. For each reporter 271 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n9 \n \nstrain, multiple biological replicates, whose results are represented by different line styles, were performed. Cell cycle 272 \ntrajectories were aligned from one mitotic exit to the next (red vertical lines at cell cycle progression values 0 and 1) and for the 273 \noccurrence of START (bright green vertical line) and budding (orange vertical line) between the two subsequent instances of 274 \nmitotic exit. Gaussian process regression was used to predict population averages of (A-B) average fluorescence intensity  of 275 \ndetected LDs, the cytoplasmic region and the whole cell mask; (C) To allow direct comparison between the cell cycle dynamics 276 \nof the fluorescence intensity of LDs detected with Pln1-mNG or mNG-Tgl3, we normalised every Gaussian process regression 277 \noutput to its own mean value and plotted the trajectories obtained with mNG-Tgl3 against those obtained with Pln1-mNG. The 278 \ngrey line indicates y = x, on which all data points would lie in case of perfect correlation. Thin coloured lines denote 279 \ncombinations of individual replicates (3x3 combinations). The thick black line represents the averaged results of the three 280 \nreplicates performed with each LD reporter protein. The circular markers on this curve represent the occurrence of the cell 281 \ncycle events mitotic exit, START and budding; (D-E) To show anticorrelation between the area-normalised LD number and the 282 \nfluorescence intensity measured on LDs, we normalised every Gaussian process output to its own mean and plotted the results 283 \nagainst each other. Thin coloured lines denote the results for the individual replicates and the thick black line represent 284 \naveraged results of the three replicates; circular markers on this black trajectory denote mitotic exit, START and budding. The 285 \ngrey line indicates where data points would lie in case of perfect anticorrelation. 286 \n 287 \nTAG storage and mobilisation give rise to LD dynamics 288 \nAfter we discovered the cell cycle dynamics of LD number and fluorescence intensity, we wondered 289 \nwhether we could identify which biological process is responsible for the oscillations. Changes in the LD 290 \nnumber and fluorescence could be due to changing synthesis and mobilisation of TAG and steryl esters , 291 \nas well as fission and fusion of LDs. To test whether TAG metabolism was responsible for the LD cell 292 \ncycle dynamics, we perturb ed TAG metabolism and subsequently observed the area-normalised LD 293 \nnumber along the cell cycle. To perturb TAG synthesis, we delet ed the genes encoding the two major 294 \nTAG synthases in S. cerevisiae Lro1 (Oelkers et al., 2000) and Dga1 (Oelkers et al., 2002). In a separate 295 \nstrain, we deleted the genes encoding the TAG lipases Tgl3 (Athenstaedt & Daum, 2003) and Tgl4 296 \n(Athenstaedt & Daum, 2005), thereby blocking the mobilisation of TAG from LDs. Notably, in the cells 297 \nwith perturbed TAG metabolism, we only used Pln1-mNG as an LD reporter, since our other reporter 298 \nprotein, Tgl3, was deleted in one of the TAG mutant strains. 299 \nBefore investigating the LD cell cycle dynamics in the two double deletion strains, we first assessed how 300 \nthe perturbation of TAG metabolism affect ed the LD phenotype independent of the cell cycle stage. To 301 \nthis end, we analysed fluorescence microscopy snapshots of cells from exponential cultures. W e saw 302 \nthat fluorescence intensity of Pln1-mNG both within the entire cell mask and on LDs was higher in 303 \nΔTGL4ΔTGL3 compared to the wild type, and lower in ΔDGA1ΔLRO1 (Figure S4). These changes in the 304 \nexpression of Pln1, which is important for the formation and stabilisation of LDs, imply that the number 305 \nand size of LDs  could also be different in the two mutation strains . Indeed, we found that the area-306 \nnormalised LD number was significantly lower in both deletion strains relative to the wild type (Figure 307 \n4A), with no puncta detected in 44% of ΔDGA1ΔLRO1 cells and 11% of ΔTGL3ΔTGL4 cells. Moreover, the 308 \nLDs in both deletion strains were bigger than those in the wild type (Figure 4B). Therefore, perturbation 309 \nof TAG metabolism, preventing its synthesis or mobilisation, affects both the number of LDs and their 310 \nsize. 311 \nSome of the changes in the LD phenotype may at first glance seem counterintuitive, but are in line with 312 \nprevious research. In t he ΔDGA1ΔLRO1 mutant, which is unable to synthesise TAG, we found larger LDs 313 \nthan in the wild type (Figure 4B) while one may expect a decrease in LD size due to the absence of TAG. 314 \nThe stabilisation of specifically small LDs by diacylglycerol acyltransferases (Kovacs et al., 2021; Wilfling 315 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n10 \n \net al., 2013) could explain the increased size of LDs in ΔDGA1ΔLRO1. The other deletion strain, 316 \nΔTGL3ΔTGL4 can synthesise TAG, but cannot mobilise it from LDs. Therefore, o ne would not expect the 317 \nobserved decrease in LD number compared to the wild type (Figure 4A) . As Tgl4 is involved in the 318 \nstabilisation of nascent LDs (Wang et al., 2024), its absence in ΔTGL3ΔTGL4 may hinder LD formation, 319 \nresulting in lower LD numbers compared to the wild type. Overall, these findings show that deleting the 320 \ngenes encoding the enzymes that synthetise or mobilise TAG changes LD morphology, and can have 321 \ncounterintuitive effects due to additional functions of these enzymes. 322 \nNext, we performed time-lapse microscopy experiments to investigate the cell cycle dynamics of LDs in 323 \nthe deletion strains and thereby determine whether TAG metabolism contributes to LD dynamics. In 324 \nboth deletion backgrounds, the cell cycle oscillations of the area-normalised LD number as observed in 325 \nthe wild type were lost (Figure 4C). Similarly to the wild type, LD size was constant along the cell cycle in 326 \nboth deletion backgrounds (Figure 4D). As LD fission would lead to the appearance of two smaller LDs 327 \noriginating from one larger LD, while LD fusion would have the opposite effect, the constant LD size 328 \nalong the cell cycle makes it improbable that LD fission and fusion contribute to the cell cycle dynamics 329 \nof the LD number. In contrast, the constant area-normalised LD number in mutants unable to synthesise 330 \nor mobilise TAG suggests that TAG metabolism must give rise to the LD dynamics as observed in wild-331 \ntype cells. Hereby, we have established that TAG metabolism, instead of the storage and mobilisation of 332 \nsteryl esters or the fission and fusion of LDs, underlies the oscillations of the LD number during the cell 333 \ncycle.  334 \n  335 \n 336 \nFigure 4. TAG storage and mobilisation give rise to LD dynamics . (A-B) Area-normalised number of LDs and LD size as 337 \ndetermined from snapshots of cells from exponential cultures that express Pln1-mNG as an LD reporter in wild type, 338 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n11 \n \nΔDGA1ΔLRO1 and ΔTGL3ΔTGL4 (298, 314 and 264 cells, respectively). The median is indicated with a white diamond and an 339 \nopen circle indicates the mean. In both deletion backgrounds, the number of detected LDs per cell cross-area is significantly 340 \nlower and LDs are significantly larger than in the wild type (two-sided Mann-Whitney U-test; p < 0.05). The percentage of cells 341 \nwithout any detected LDs is indicated next to each violin in A; (C-D) Cell cycle dynamics of the area-normalised L D number and 342 \nsize of detected LDs predicted with Gaussian process regression applied to cell cycle-aligned single-cell trajectories from three 343 \nbiological backgrounds, indicated in different colours. Biological replicates are indicated by different line styles. Cell cycles were 344 \naligned from one occurrence of mitotic exit to the next (red vertical lines at cell cycle progression values 0 and 1) and for 345 \noccurrence of START (solid vertical lines) and budding (dashed vertical lines). 346 \n 347 \nPerturbing LDs through TAG metabolism delays START 348 \nFinally, we asked if the LD dynamics, as observed in the wild type but lost in the strains with perturbed 349 \nTAG metabolism, could affect cell cycle progression. Since START is delayed when cells that cannot 350 \nmobilise TAG from LDs resume growth after starvation (Kurat et al., 2009), we wondered if the same 351 \ncould be true in exponentially growing cells. To investigate this, we assessed the interrelation between 352 \nthe duration of the whole cell cycle and the time between  mitotic exit and START in individual cell 353 \ncycles. We plotted the duration of the mitotic exit to START phase against the whole cell cycle length in 354 \nΔDGA1ΔLRO1, ΔTGL3ΔTGL4 and the wild type and performed linear regression to obtain equations that 355 \ndescribe the ir interrelation (Figure 5A) . The regression lines describe the duration of mitotic exit to 356 \nSTART as a function of cell cycle length and therefore, their slopes indicate the fraction of the cell cycle 357 \ntaken up by mitotic exit to START.  We found that the slopes obtained from both deletion strains were 358 \nsteeper than those from the wild type (Figure 5B), which means that mitotic exit to START takes up a 359 \nlarger fraction of the cell cycle in TAG mutants compared to the wild type. The change in the fraction of 360 \nthe cell cycle taken up by mitotic exit to START detected on the single-cell cycle level was not 361 \naccompanied by extensive population-level changes in absolute duration of the cell cycle or its phases 362 \nmitotic exit to START, START to budding and budding to mitotic exit (Figure S5). The absolute changes in 363 \nduration between the wild type and the two TAG mutants were comparable for the cell cycle phases and 364 \nthe whole cell cycle and on average were equal to 5 minutes, which corresponds to one imaging 365 \ninterval. Together , these results show that in cell cycles of identical length, START occurs later in 366 \nΔDGA1ΔLRO1 and ΔTGL3ΔTGL4 than in the wild type. This suggests that START is delayed in cells that 367 \ncannot synthesise or mobilise TAG, which signifies that intact TAG metabolism is important for the 368 \ntimely occurrence of START.   369 \n 370 \n 371 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n12 \n \nFigure 5. Perturbing LDs through TAG metabolism delays START. (A) Interrelation between the duration of mitotic exit to START 372 \nand duration of the whole cell cycle in the wild type, ΔDGA1ΔLRO1 and ΔTGL3ΔTGL4. Different colours represent replicate 373 \nexperiments and marker size scales with the number of times a combination of cell cycle length and mitotic exit to START 374 \nduration was observed. Trend lines obtained with linear regression describe the duration of mitotic exit to START duration as a 375 \nfunction of cell cycle length ; (B) Variation in the slope values of the regression lines from A, estimated with bootstrapping. A 376 \ntotal of 100 bootstrapping iterations were performed for every experiment. In each iteration, 50%  of data points were 377 \nrandomly sampled with replacement and subsequently, linear regression was performed to obtain a slope value. Mean and 378 \nstandard deviation of the slope values obtained with bootstrapping are shown in black. Grey horizontal lines indicate the slo pe 379 \nvalues of the regression lines in A, which were obtained using all data points. 380 \n 381 \nDISCUSSION 382 \nIn this work, we established Pln1, Tgl3 and Rrt8 as marker proteins for LDs by showing their 383 \ncolocalisation with LDs stained with BODIPY-TR. Using Pln1-mNeonGreen and mNeonGreen-Tgl3 as LD 384 \nreporters in time-lapse microscopy experiments, we found that the ar ea-normalised LD number 385 \noscillates during the cell cycle, with a minimum around START and a peak halfway through S/G2/M . We 386 \nperturbed TAG metabolism by deleting the genes that encode the acyl transferases Dga1 and Lro1 or the 387 \nlipases Tgl3 and Tgl4, respectively preventing the synthesis of TAG or its mobilisation from LDs. Both sets 388 \nof gene deletions abolished the cell cycle dynamics in the area-normalised LD number. Furthermore, in 389 \ncell cycles of identical duration, START on average was delayed in the two double deletion backgrounds 390 \ncompared to the wild type , suggesting that the mobilisation of TAGs from LDs is important for START to 391 \ntake place. Thus, our results demonstrate the importance of storage lipid metabolism for cell cycle 392 \nprogression. 393 \nThe LD cell cycle dynamics that we observed are supported by a number of previous publications, 394 \nindicating biological processes that m ay contribute to the dynamic behaviour of LDs . First, cells that re-395 \nenter the cell cycle after starvation mobilise neutral lipids from LDs (Kurat et al., 2006; Rajakumari & 396 \nDaum, 2010). As we have shown here, exponentially growing cells do the same when passing START. 397 \nFurthermore, both the protein levels of the fatty acid synthesis enzymes Acc1, Fas1 and Fas2 (Blank et 398 \nal., 2017) and lipid biosynthetic activity (Takhaveev et al., 2023) peak in S/G2/M, which could explain the 399 \nincrease in area-normalised LD number we observed during this part of the cell cycle. Moreover, the 400 \nlipids that are mobilised from LDs during the second half of S/G2/M could partake in triglyceride cycling, 401 \na process in which TAG is partially degraded and then re-synthesised with different fatty acid chains, to 402 \nmetabolically alter stored neutral lipids and change their exact molecular identity  (Wunderling et al., 403 \n2023). Finally, neutral lipid storage is important right before mitotic exit, as cells that lack all LDs exhibit 404 \ncytokinetic defects, which are rescued by chemical inhibition of lipid synthesis (P. L. Yang et al., 2016). 405 \nHowever, a recent study that quantified TAG in cells going through the yeast metabolic cycle (YMC) (S. 406 \nYang et al., 2025) contradicts the neutral lipid storage dynamics inferred from the LD number in the 407 \ncurrent work. Yang et al.  show an increase in TAG levels early in the YMC, followed by a gradual 408 \ndecrease that lasts until the cycle is completed. We believe that the discrepancy between the LD 409 \ndynamics we report and these TAG dynamics along the YMC could be due to only 40% of these 410 \nmetabolically synchronised cells dividing. Since LDs are dynamic during stationary phase (Hariri et al., 411 \n2018; Qiu et al., 2023; Wang et al., 2014) when cells no longer divide, the dividing and non-dividing cells 412 \nmay well have distinct TAG dynamics . In this case, the reported TAG dynamics along the YMC would not 413 \nmatch the cell cycle dynamics of TAG, which in turn could explain the differences between the dynamics 414 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n13 \n \nof TAG (S. Yang et al., 2025) and the cell cycle dynamics of the area-normalised LD number reported in 415 \nthe current work. 416 \nMultiple cell cycle regulators could be involved in the orchestration of LD dynamics. The cyclin-417 \ndependent kinase Cdc28 activates the lipase Tgl4 (Kurat et al., 2009) and inhibits Pah1 (Choi et al., 2011; 418 \nSantos-Rosa et al., 2005), which synthesises th e TAG precursor diacylglycerol. Pah1 is also regulated by 419 \nPKA (Su et al., 2018) and PKC (Su et al., 2014) . Activity of TORC1 along the cell cycle (Guerra, 420 \nVuillemenot, Van Oppen, et al., 2022) coincid es with periods of mobilisation of neutral lipids from LDs . 421 \nIndeed, TORC1 activity has been shown to stimulate the mobilisation of neutral lipids from LDs  (Madeira 422 \net al., 2014). Thus, the cell cycle regulators Cdc28, PKA, PKC and TORC1 conceivably could participate in 423 \nthe coordination of the cell cycle dynamics of LDs. 424 \nWe have shown that mobilisation of TAG from LDs contributes to the timely occurrence of START. 425 \nPrevious findings indicate that the mobilised lipids could be used as precursors for sphingolipid 426 \nsynthesis. Supplementation with a sphingolipid precursor rescues the delay in START in ΔTGL3ΔTGL4 427 \n(Chauhan et al., 2015) while inhibition of sphingolipid synthesis inhibits the G1/S transition (Cerbón et 428 \nal., 2005). Sphingolipids can activate the phosphatase PP2A Cdc55 (Nickels & Broach, 1996), which in turn 429 \npromotes START (McCourt et al., 2013; Moreno-Torres et al., 2015). Thus, sphingolipid synthesis can 430 \ndrive cell cycle progression and lack thereof could explain the delay in START observed in TAG mutant 431 \nstrains. 432 \nOverall, we have discovered that the number of LDs oscillates along the cell cycle , that this oscillation 433 \ndepends on TAG metabolism and that the mobilisation of TAG from LDs is important for the timely 434 \noccurrence of START. Our findings highlight the importance of storage lipid metabolism in cell cycle 435 \nprogression and emphasise that LD storage should be considered in research centred on commitment to 436 \nthe cell cycle. Further research is still needed to elucidate the regulatory processes underlying the 437 \nobserved dynamics of LDs. 438 \n 439 \nMATERIALS AND METHODS 440 \nStrains 441 \nThe yeast strains used in this study (Table S1) were constructed from the wild-type strain YSBN6 (S288C 442 \nbackground) (Canelas et al., 2010) or from YSBN6 WHI5::mCherry-BLE (Litsios et al., 2019), using a 443 \nCRISPR-Cas9 based approach (Novarina et al., 2022) with primers listed in Table S2. To tag proteins with 444 \nthe fluorescent protein mNeonGreen, we used a codon-optimised sequence for expression in S. 445 \ncerevisiae (Guerra, Vuillemenot, Rae, et al., 2 022). Gene deletions were verified by PCR. Introduction of 446 \na fluorescent protein on target proteins was verified by PCR and sequencing. 447 \nCulturing 448 \nCells were grown in 10 mL of Verduyn minimal medium (Verduyn et al., 1992) buffered at pH 5.0 with 10 449 \nmM potassium phthalate and with 2% glucose as a carbon source in 100 mL flasks at 30°C, under 450 \nconstant rotation at 300 rpm. To obtain snapshots of live cells, 2-4 µL of cells were taken from an 451 \nexponentially growing culture and placed on a microscopy slide under a 1% agarose pad soaked with the 452 \nculture medium. To prepare cells for microfluidic experiments, cultures were maintained in the 453 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n14 \n \nexponential phase for at least 12 h prior to the experiment through repeated dilution. On the day of the 454 \nexperiment, cultures were diluted to an OD600 of 0.05 and after one additional doubling, cells were 455 \nloaded into the microfluidic chip. Microfluidics experiments were performed as described (Huberts et 456 \nal., 2013). The flow rate during microfluidics experiments was 4 µL/min.  457 \nFormaldehyde fixation and BODIPY-TR staining 458 \nCells were grown for 24 h to reach stationary phase. The equivalent of 1 mL of a culture with an OD 600 of 459 \n5 was harvested by centrifugation (3 min, 10 000 g). Next, cells were fixed with formaldehyde as 460 \ndescribed (Madeira et al., 2015). Briefly, cells were resuspended in 1 mL of 3.7% (w/v) 461 \nparaformaldehyde with 0.1 M sorbitol as an osmolyte and incubated at room temperature for 15 min. 462 \nAfterwards, cells were washed once with 1 mL of phosphate buffered saline (PBS) and finally 463 \nresuspended in 1 mL of PBS.  464 \nTo stain formaldehyde-fixed cells with BODIPY-TR methyl ester (Lumiprobe GmbH) 1 µL of 5 mM 465 \nBODIPY-TR methyl ester dissolved in DMSO was added to 100 µL of fixed cell suspension and incubated 466 \nat room temperature for 5 min. Cells were then pelleted (1.5 min, 12 500 g), washed once with 100 µL of 467 \nPBS and finally resuspended in 100 µL of PBS. Cells were imaged immediately after staining. 468 \nMicroscopy 469 \nAll images were acquired on a Nikon Eclipse Ti-E inverted wide-field fluorescence microscope equipped 470 \nwith the Nikon Perfect Focus System (PFS) and an Andor-DU -897 EX camera. For imaging, a 100x S Fluor 471 \nOil objective (NA 1. 4, Nikon) was used and the readout mode was set to 1 MHz without gain 472 \namplification. For bright-field images, excitation was done with a halogen lamp fitted with a 420 nm 473 \nbeam-splitter to filter out short wavelengths. A Lumencor AURA excitation system was used for 474 \nfluorescence excitation. Green fluorescent proteins were excited at 485 nm with an imaging set-up 475 \nconsisting of a 470/40 nm band-pass filter, a 495 nm beam splitter and a 525/50 nm emission filter. Red 476 \nfluorophores were imaged using excitation at 565 nm, a 560/40 nm band-pass filter, a 585 nm beam 477 \nsplitter and a 630 nm/75 nm emission filter. Intensity of the light source and excitation time for each 478 \nfluorophore and each experiment are detailed in Table S3. For live-cell imaging, the microscope setup 479 \nwas kept at 30 °C using an incubator box (Life Imaging Services). 480 \nOptimising image alignment 481 \nTo correct the small-scale but systematic misalignment between images recorded in the GFP channel 482 \n(mNeonGreen-tagged proteins) and the RFP channel (BODIPY-TR), we used the bilinear transformation 483 \napproach from the Python module pyStackReg (Thévenaz et al., 1998) . To obtain the transformation 484 \nmatrix, we created composite images of binarised snapshot images of cells that express Pln1-485 \nmNeonGreen and that were also stained with the red fluorophore BODIPY-TR. Binarisation of the 486 \nsnapshot images ensured that only outlines of the cells, but no cytoplasmic structures, were visible. 487 \nCombination of multiple snapshot images yielded a composite image showing the outline of cells 488 \ndispersed over the whole field of view. We aligned the composite of RFP snapshots to the composite of 489 \nthe corresponding GFP snapshots, thereby obtaining a transformation matrix which we used to 490 \noptimally align images recorded in the RFP channel to images recorded in the GFP channel. 491 \nCell segmentation and cell cycle alignment 492 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n15 \n \nFluorescence images were background corrected using the rolling ball algorithm implemented in ImageJ 493 \nwith a radius of 50 pixels. Cell masks were obtained from bright-field images with the ImageJ plugin BudJ 494 \n(Ferrezuelo et al., 2012). Segmentation was inspected visually to verify that the cell masks match ed the 495 \ncells in the bright-field images. Finally, cell cycles were excluded from further analysis if their total 496 \nduration, from one mitotic exit to the next, exceeded 180 min. For all cells, we manually tracked mitotic 497 \nexit, budding and START. Budding was detected in the bright-field images, START was detected from 498 \nWhi5-mCherry leaving the nucleus and mitotic exit from Whi5-mCherry entering the nucleus. 499 \nFor the cell cycles that passed the selection criteria ( i.e. proper segmentation and cell cycle duration), 500 \nwe transformed the data onto a common cell cycle progression coordinate ranging from 0 to 1 to allow 501 \ncomparison between cycles of different durations. Consecutive mitotic exits were defined as 0 and 1. 502 \nWe also determined the average position of START and budding on this common time coordinate. To do 503 \nso, we calculated the fraction of the cell cycle that had passed at the moment that each event occurred. 504 \nSpecifically, to determine the timing of START, we divided the time between the first mitotic exit and 505 \nSTART by the duration of the whole cell cycle. We repeated this procedure for budding. Ultimately, 506 \ncombining data from all cell cycles, we determined the average timing of START and budding on the 507 \nnormalised cell cycle progression coordinate. 508 \nSubsequently, we determined the cell cycle progression coordinate value for every data point recorded 509 \nin the time-lapse microscopy experiments. To do this, we divided each cycle into three phases: mitotic 510 \nexit to START, START to budding and budding to mitotic exit. For each of these phases, we placed its first 511 \nand last data point, which coincide with a cell cycle event, at the normalised time values for these 512 \nevents. Then, we evenly dispersed all interstitial data points over the time interval bounded by the two 513 \ncell cycle events. Performing this procedure for every cell cycle, we determined a time value on the 514 \ncommon cell cycle progression coordinate for every recorded data point. 515 \nTo infer the population-average behaviour of the various measures over the cell cycle, we performed 516 \nGaussian process regression of the cell cycle aligned single-cell data. For this, we used Python’s 517 \nsklearn.gaussian_process (Pedregosa et al., 2011) using the radial basis function (RBF) as a 518 \nprior, with the length scale range [0.01, 0.5] and the white kernel with free noise level. An optimised fit 519 \nwas obtained through maximisation of the log-marginal likelihood in the regression. 520 \nAutomated detection of LDs in fluorescence microscopy images 521 \nWe used the PunctaFinder algorithm (Terpstra et al., 2024) to automatically detect LD puncta and 522 \nestimate their size, both for LDs stained with BODIPY-TR in the RFP channel and for LDs visualised with 523 \nfluorescently tagged marker proteins in the GFP channel. We used an overlap parameter value of zero 524 \nand a punctum diameter of three pixels, which reflects a distance of 480 nm on our microscopy setup 525 \nand corresponds to the diameter of an average lipid droplet ( i.e. 400 nm) (Czabany et al., 2008). To 526 \nobtain suitable threshold values for punctum detection, we created a manually curated data set of 40 527 \ncells for each genetic background and performed threshold value optimisation with five bootstrap 528 \niterations, sampling 75% of cells with replacement. Each final threshold value was the average of the 529 \nvalues obtained in the five bootstrap iterations. The threshold values are provided in Table S4. 530 \nColocalisation analysis 531 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n16 \n \nColocalisation between puncta of LDs stained with BODIPY-TR and puncta of LD marker proteins tagged 532 \nwith mNeonGreen was assessed based on the distance between the midpoints of puncta in the two 533 \nimaging channels. Puncta are qualified as colocalising if their midpoints are maximally one pixel apart in 534 \nboth the x- and y- direction. 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It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n23 \n \nSUPPLEMENTARY INFORMATION 768 \n 769 \nFigure S1. BODIPY-TR fluorescence is not detected in the GFP channel and puncta of GFP 770 \nautofluorescence are not detected in cells expressing an LD reporter tagged with mNeonGreen. (A) 771 \nColocalisation between LDs stained with BODIPY-TR and puncta detected in the GFP channel in wild-type 772 \ncells. The left circle of the Venn diagram represents all LDs identified with BODIPY-TR in the RFP channel; 773 \nthe right circle represents all puncta identified in the GFP channel. The orange overlapping region are 774 \npuncta that colocalise between the LDs stained with BODIPY-TR and the puncta in the GFP channel. The 775 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n24 \n \nred region indicates LDs that do not colocalise with a punctum in the GFP channel, while the green 776 \nregion indicates puncta in the GFP channel that do not colocalise with an LD stained with BODIPY-TR. 777 \nThe white regions represent puncta with ambiguous colocalisation: colocalisation between puncta in the 778 \ntwo fluorescence channels is only probable, not certain, despite their midpoints being close together. 779 \n274 cells were analysed, 427 BODIPY-TR puncta and 89 puncta in the GFP channel were identified; (B) 780 \nComparison of the average fluorescence intensity measured in the GFP channel for cells stained with 781 \nBODIPY-TR and unstained cells. Since the cells stained with BODIPY-TR are not brighter than the 782 \nunstained cells, we can conclude that BODIPY-TR fluorescence is not detected in the GFP channel ; (C) 783 \nFluorescence microscopy images of wild-type cells, recorded in the GFP channel . The top row shows 784 \nunstained cells, while the bottom row shows cells stained with the red fluorophore BODIPY-TR . There 785 \nare no clearly visible differences between stained and unstained cells, demonstrating that BODIPY-TR 786 \nstaining does not influence images recorded in the GFP channel. Furthermore, puncta could be detected 787 \nin images of both stained and unstained cells, as shown in the third column of images. This finding 788 \nindicates that brighter spots in the autofluorescence occur naturally and can result in the appearance of 789 \npuncta; (D) Fluorescence microscopy images and detected puncta of three wild-type cells stained with 790 \nBODIPY-TR. The puncta identified in the GFP channel images are only slightly brighter than the 791 \ncytoplasm and, in the visualised cells as well as the majority of other cells, do not colocalise with 792 \nBODIPY-TR puncta, ruling out that these GFP puncta are due to detection of BODIPY-TR signal in the GFP 793 \nchannel; (E-F) Average fluorescence intensity of the cytoplasm of cells with at least one punctum, the 794 \nsame whole cells, i.e. cytoplasm and puncta combined, and the detected puncta. Shaded areas in E and 795 \nF indicate the box (first quartile to third quartile) of whole wild-type cells or puncta detected in wild-796 \ntype cells, respectively. The punctum detection threshold values to detect GFP puncta in wild-type cells 797 \nare more stringent than those applied to cells expressing mNG-Tgl3 or Rrt8-mNG (Table S4) . Still, the 798 \naverage fluorescence intensity of the cytoplasm is significantly lower than that of whole cells in the 799 \nthree reporter strains, but not in the wild-type control (one-sided Mann-Whitney U-test, p<0.01). This 800 \nfinding indicates that the GFP autofluorescence puncta detected in the wild type are similar to the 801 \ncytoplasm with regards to fluorescence intensity, while in the three LD reporter strains, the bright 802 \npuncta cause the average fluorescence intensity of whole cells to be higher than that of the cytoplasmic 803 \nregion alone. Moreover, the average fluorescence intensity of the GFP autofluorescence puncta 804 \ndetected in the wild type is notably lower than that of puncta detected in any of the three LD reporter 805 \nstrains. Together, these results indicate that it is improbable that GFP autofluorescence puncta are 806 \ndetected in the three LD reporter strains. 807 \n  808 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n25 \n \n 809 \nFigure S2. Cell cycle length increases with experiment duration in time-lapse imaging of Rrt8-mNG but 810 \nnot Pln1-mNG and mNG-Tgl3. (A) Cell cycle length, from one instance of mitotic exit (ME) to the next 811 \nwas plotted against the starting time of the cycle within the time-lapse experiment for cells expressing 812 \nPln1-mNG, mNG-Tgl3 or Rrt8-mNG as a reporter protein for LDs. Replicate experiments are shown with 813 \ndistinct colours . Trend lines describing cell cycle length as a function of cycle initiation time were 814 \nobtained with linear regression, which was performed on pooled data of replicate experiments; (B) 815 \nBootstrapping was performed to estimate the variation in the slope of the regression lines from A. For 816 \neach bootstrap iteration, 50% of the data was randomly sampled with replacement and linear regression 817 \nwas performed to obtain a slope value; a total of 100 iterations were performed for each genetic 818 \nbackground. Grey horizontal lines indicate the slope values of the regression lines in A, which were 819 \nobtained using all data points. Mean and standard deviation of the slopes obtained with bootstrapping 820 \nare indicated in black. Interestingly, regression lines fitted to data from cells expressing Rrt8-mNG ha ve 821 \npositive slope of values, while regression lines fitted to data from cells expressing Pln1-mNG or mNG-822 \nTgl3 have an average slope value of approximately 0. Thus, in time-lapse imaging of cells expressing 823 \nRrt8-mNG cell cycle length increases the longer the experiment has lasted. This reveals a potential 824 \nphototoxic effect, caused by the cumulative effects of repeated exposure to the lasers used for 825 \nfluorophore excitation. 826 \n  827 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n26 \n \n 828 \nFigure S3. LD number is dynamic along the cell cycle while LD size is constant.  LDs were identified in 829 \ntime-lapse microscopy images of cells expressing either Pln1-mNG (A, C, E) or mNG-Tgl3 (B, D, F) as an 830 \nLD marker protein. For both reporter strains, three replicate experiments were performed. Cell cycle 831 \ntrajectories were aligned from one occurrence of mitotic exit (ME) to the next (red vertical lines at cell 832 \ncycle progression values 0 and 1) and were also aligned for START (bright green vertical line) and 833 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n27 \n \nbudding (orange vertical line). (A-D) Density estimations, showing densely populated data points in red 834 \nand sparsely populated data points in blue, obtained with Gaussian kernel estimation , show the cell 835 \ncycle dynamics of (A , B) the number of LDs normalised to the cell cross-area and (C , D) LD size. The 836 \nnumber of cell cycles assessed in each replicate is indicated on the right side of each plot. The plots in 837 \neach second column zoom in on the plots with all data and show their  most densely populated region, 838 \nlocated between the dashed horizontal lines . Here, t he colour map has been rescaled to assess the 839 \ndensity in more detail. Notably, the density plots show the same cell cycle dynamics of the LD number 840 \nnormalised to the cell cross-area predicted with Gaussian process regression (Figure 2A)  when Pln1-841 \nmNG is used as an LD reporter protein, but not with mNG-Tgl3. Still, with mNG-Tgl3 as an LD marker, 842 \nrelatively dense subpopulations with <0.1 LDs/µm 2 are visible early in the cell cycle in all three 843 \nreplicates, reflecting the trough around START in the cell cycle dynamics of the number of LDs per cell 844 \ncross-area predicted with Gaussian process regression (Figure 2 B). Also, with mNG- Tgl3 as an LD 845 \nreporter protein, distinct subpopulations of cells with one or two puncta are visible, at #LD/area values 846 \nof approximately 0.07 LDs/µm -2 and 0.14 LDs/µm -2, respectively. These subpopulations occur since the 847 \narea-normalised LD number is obtained by dividing the discrete number of LDs by the continuous cell 848 \ncross-area. Evidently, the range in cell cross-area values is narrow, causing the resulting area-normalised 849 \nLD number to still appear discrete in the density plots; (E-F) Gaussian process regression outputs 850 \nshowing the cell cycle dynamics of the total area of detected LDs , i.e. summed sizes of all LDs detected 851 \nin a cell, without normalisation to the cell cross-area. Since these outputs resemble the cell cycle 852 \ndynamics of the area-normalised LD number, its oscillation does not result from the normalisation to the 853 \ncell cross-area. Moreover, the strong resemblance between the cell cycle dynamics of the summed LD 854 \nsize and the dynamics of the area-normalised LD number further confirms that LD number, but not LD 855 \nsize, is dynamic along the cell cycle.  856 \n  857 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n28 \n \n 858 \nFigure S4. Genetic perturbation of TAG metabolism affects Pln1-mNG expression levels. TAG metabolism 859 \nwas perturbed by gene deletion of either DGA1 and LRO1, which encode the main TAG synthases, or 860 \nTGL3 and TGL4, which encode the lipases responsible for TAG mobilisation from LDs. Average Pln1-861 \nmNeonGreen fluorescence intensity was determined in snapshot images of cells from an exponential 862 \nculture for (A) the whole cell  and the cytoplasmic region as well as (B) the LDs in the wild type, 863 \nΔDGA1ΔLRO1 and ΔTGL3ΔTGL4. White diamonds indicate the median and open circles indicate the 864 \npopulation average. Both genetic perturbations led to significant changes in measured Pln1-865 \nmNeonGreen fluorescence compared to the wild type (two-sided Mann-Whitney U-test, p < 0.001) for 866 \nall three regions of interest. Notably, the change in Pln1-mNG fluorescence compared to the wild type is 867 \nmuch larger for ΔDGA1ΔLRO1 than for ΔTGL3ΔTGL4, as quantified using Cohen’s d to assess the effect 868 \nsize. For ΔDGA1ΔLRO1, the effect size for the changing fluorescence intensity between deletion strain 869 \nand wild type was 2.54 , 2.40 and 1.86 for the whole cell, the cytoplasm and the puncta, respectively. In 870 \ncontrast, for ΔTGL3ΔTGL4, the effect size for these three regions of interest was equal to 0.54, 0.49 and 871 \n0.79. 872 \n  873 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n29 \n \n 874 \nFigure S5 . Duration of the cell cycle and its subphases are altered slightly in ΔDGA1ΔLRO1 and 875 \nΔTGL3ΔTGL4 compared to the wild type. Probability density functions for duration of  (A) the whole cell 876 \ncycle and the cell cycle phases (B) mitotic exit to START, (C) START to budding and (D) budding to mitotic 877 \nexit in the wild type, ΔDGA1ΔLRO1 and ΔTGL3ΔTGL4. To obtain these distributions, cell cycles recorded 878 \nin three replicate experiments with the wild type and two replicate experiments each with ΔDGA1ΔLRO1 879 \nand ΔTGL3ΔTGL4 were pooled. Solid and dotted vertical lines denote the mean and median value of 880 \neach distribution, respectively . Percentages denote the change in median and mean in each deletion 881 \nstrain compared to the wild type. Above each plot, a schematic representation of the cell cycle indicates 882 \nthe cell cycle phase(s) studied. Notably, only cell cycles with a total duration of at most 180 min were 883 \nincluded in the analysis. 884 \n  885 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n30 \n \nTable S1. Yeast strains used in the current study. 886 \nStrain Source \nYSBN6 (S288C-derived strain, MATa FY3 HO::HphMX4) Canelas et al., 2010 \nYSBN6 WHI5::mCherry-BLE Litsios et al., 2021 \nYSBN6 PLN1::mNeonGreen This study \nYSBN6, TGL3::mNeonGreen-Tgl3 This study \nYSBN6 RRT8::mNeonGreen This study \nYSBN6 WHI5::mCherry-BLE PLN1::mNeonGreen This study \nYSBN6 WHI5::mCherry-BLE TGL3::mNeonGreen-Tgl3 This study \nYSBN6 WHI5::mCherry-BLE RRT8:mNeonGreen This study \nYSBN6 WHI5::mCherry-BLE PLN1::mNeonGreen ΔDga1 ΔLro1 This study \nYSBN6 WHI5::mCherry-BLE PLN1::mNeonGreen ΔTgl3 ΔTgl4 This study \n 887 \nTable S2. Primers used in the current study.  888 \nName Sequence Application \nPLN1_sg_fwd gactttCTAATTGGTCGACACAGCCG Primers with sgRNA guide \nsequences targeting the \nspecified genes within the S. \ncerevisiae genome. Upper-case \nnucleotides encompass the \nactual guide sequences, while \nlower-case nucleotides \ncomprise adapters that allow \nplasmid assembly in a \nGoldenGate Assembly \napproach. \nPLN1_sg_rev aaacCGGCTGTGTCGACCAATTAGa\na \nTGL3_sg_fwd gactttTGAGTTGCCGTTAAGCATGA \nTGL3_sg_rev aaacTCATGCTTAACGGCAACTCAaa \nRRT8_sg_fwd gactttTGGTGTACTTCGCTACTAAA \nRRT8_sg_rev aaacTTTAGTAGCGAAGTACACCAa\na \nTGL4_sg_fwd gactttTTTACTCAATAAGAAAACAC \nTGL4_sg_rev aaacGTGTTTTCTTATTGAGTAAAaa \nDGA1_sg_fwd gactttTTGGGTAATAATGAATTCAT \nDGA1_sg_rev aaacATGAATTCATTATTACCCAAaa \nLRO1_sg_fwd gactttGATGGATAGTGAGTCAATGT \nLRO1_sg_rev aaacACATTGACTCACTATCCATCaa \nPLN1_repair_fwd TGGGCAATGCCACCATTGAGAAGC\nTAAAGGCCTCAAGAGAAGACCAAA\nCCAATTCTAAGCCAGCGGCTGTGT\nCGACCAATATGGTGAGCAAGGGC\nGAG \nPrimers to create repair \nfragments that introduce an \nmNeonGreen tag on the \nspecified target proteins with \nCRISPR-Cas9 assisted cutting. \nTemplate in the PCR is the DNA \nsequence encoding \nmNeonGreen, codon-optimised \nfor S. cerevisiae. \nPLN1_repair_rev TAACTATATAAGAGTGGCAGGAAA\nAAAAATCAGGCGCACGATTAGCGC\nAAAACCAAATTATTACTTGTACAGC\nTCGTCCATGC \nTGL3_tag_repair_fwd ATGACACAATAGTAAGGGAATCAT\nCTATTCATATATCACATCTTTGAGTT\nGCCGTTAAGCATGGTGAGCAAGG\nGCGAG \nTGL3_tag_repair_rev GTATCCAGTTTTTCAAAAGGGTCG\nGTATTACAGCAGACACCTTGTATTC\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n31 \n \nCTGCGCCGTTTCCTTCATCTTGTAC\nAGCTCGTCCATGCCC \nRRT8_repair_fwd AATAGTCACCATATCTAGCAACACT\nGTTGGTGCAGCTAAATGGTGTACT\nTCGCTACTGAAGGGTGAAAGAAAG\nAAGGAAATGGTGAGCAAGGGCGA\nG \nRRT8_repair_rev GATTAACAATTAGTTAAGGAATAT\nATAATCACACTTCTACATAAATTTG\nCTGTTTTAGGCTTACTTGTACAGCT\nCGTCCATGC \nPLN1_rep_short_fwd GGTTGGACTTGGGCAATG Primers to amplify repair \nfragments that introduce an \nmNeonGreen tag on the \nspecified target protein. \nTemplate in the PCR is genomic \nDNA from a strain that already \nexpresses the tagged target \nprotein. \nPLN1_rep_short_rev CAAATAACTATATAAGAGTGGCAG\nG \nTGL3_rep_short_fwd ATGACACAATAGTAAGGGAATCAT\nC \nTGL3_rep_short_rev CATACACTACACGCAGTATCCAG \nRRT8_rep_short_fwd AATAGTCACCATATCTAGCAAC \nRRT8_rep_short_rev GAACTTGATTAACAATTAGTTAAG\nG \nTGL3_deletion_repair_fwd AGTAAGGGAATCATCTATTCATATA\nTCACATCTTTGAGTTGCCGTTAAGC\ntatcgtttccacttttttctgtc \nPrimers to amplify repair \nfragments that delete the gene \nof interest from the S. cerevisiae \ngenome after CRISPR-Cas9 \nassisted cutting. Nucleotides \nshown in lower case are reverse \ncomplementary to the other \nprimer for amplification in a \ntemplate-free PCR. \nTGL3_deletion_repair_rev ATCGAGCTCTATCAATAAAAAAAA\nTAAGACAGAAAAAAGTGGAAACG\nATAgcttaacggcaactcaaagatg \nTGL4_repair_fwd CGCTGTAATAATTATTGAAGGGAG\nTACAGGTATATGTAATAAAAGTCT\nGAgaaaacacgggcttg \nTGL4_repair_rev GGCCATTCGAATAAATACATAGAT\nGAAAAAGAATATCTAGAGGATATA\nTAAGCAAGCCCGTGTTTTCtcagactt\nttattacatatacctg \nDGA1_repair_fwd TACATATACATAAGGAAACGCAGA\nGGCATACAGTTTGAACAGTCACAT\nAAtaataatgaattcattggaaaac  \nDGA1_repair_rev CTTAAGATATACAGCCCAAACACTA\nAAAAATCCTTATTTATTCTAACATA\nTTTTGTGTTTTCCAATGAATTCATTA\nTTA \nLRO1_repair_fwd CCATTACAAAAGGTTCTCTACCAAC\nGAATTCGGCGACAATCGAGTAAAA\nAtaaatgaccgacattgactcactatc \nLRO1_repair_rev GCGACGCGCCTTCTTTTCGCTCTTT\nGAAATAATACACGGATGGATAGTG\nAGTCAATGTCGGTCATTTA \nPLN1_check_fwd CGAAACCTACCAACGCTTCAC Primer pairs to verify that the \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n32 \n \nPLN1_check_rev GTCTCTTGATCGAGCTATAACC desired genomic modifications \nwere successful. TGL3_tag_check_fwd CCTAGGTCTGAAAATTCAACCC \nTGL3_tag_check_rev ATGACTCTTGAGTGTGGCCG \nRRT8_check_fwd CATATGTTTCGGTATGTCTGCC \nRRT8_check_rev GACGAGCAAGTTTTATCGAACG \nTGL3_deletion_check_fwd AGATACTTATCCTAGGTCTG \nTGL3_deletion_check_rev CTGAATGAGAAGGAGTCAAC \nTGL4_check_fwd TAATTGCGACTATGAAACGC \nTGL4_check_rev ACCAATATCTTTCTTCCACC \nDGA1_check_fwd CTTTCACTACACTTCCGCCAAAG \nDGA1_check_rev CCTAAACTTACATTCAAACAACTTC \nLRO1_check_fwd CCAACTACTTAGTGTAGATC \nLRO1_check_rev CTCCTCTATCTACTGTCGTTTG \nmNG_seq_rev CCATCATTAGGGTTACCTG Sequencing primers to verify \nthat the DNA sequence \nencoding mNeonGreen has \nbeen integrated at the target \nsite correctly. The reverse \nprimer anneals approximately \n130 bp from the beginning of \nmNeonGreen, while the \nforward primer anneals \napproximately 170 bp before its \nend. \nmNG_seq_fwd GCTAGAACAACGTACACATTCG \n 889 \nTable S3. Imaging settings used for microscopy experiments. For each fluorophore and protein of 890 \ninterest, the imaging channel, light intensity and excitation time applied are detailed below. 891 \nTarget Imaging channel Light intensity Excitation time \nBODIPY-TR (fixed cells) RFP 1% 5 ms \nPln1-mNG (fixed cells) GFP 7% 300 ms \nmNG-Tgl3 (fixed cells) GFP 7% 300 ms \nRrt8-mNG (fixed cells) GFP 7% 300 ms \nautofluorescence (fixed cells) GFP 7% 300 ms \nWhi5-mCherry (live cells; time-lapse) RFP 10% 300 ms \nPln1-mNG (live cells; time-lapse) GFP 3% 200 ms \nmNG-Tgl3 (live cells; time-lapse) GFP 3% 200 ms \n 892 \nTable S4. Threshold values used for automated punctum detection with PunctaFinder . Threshold values 893 \nfor punctum detection in fluorescence microscopy images of the neutral lipid dye BODIPY-TR (RFP 894 \nchannel) and mNeonGreen (mNG) tagged reporter proteins for LDs or a wild-type autofluorescence 895 \ncontrol (GFP channel). Because of slight differences in BODIPY-TR staining between the four samples, 896 \nseparate thresholds were determined for punctum detection in the RFP channel as well. In all cases, the 897 \npunctum diameter was set to three pixels and the overlap parameter value to zero. Thresholds were 898 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint \n\n33 \n \ndetermined based on manually validated datasets of 42 cells expressing Pln1-mNG, 41 cells expressing 899 \nmNG-Tgl3, 43 cells expressing Rrt8-mNG and 45 wild-type cells.   900 \nGenetic \nbackground \nFluorescence \nchannel \nT ratio, local T ratio, global T CV \nPln1-mNG GFP 1.408 1.406 0.358 \nRFP 1.252 1.338 0.262 \nmNG-Tgl3 GFP 1.342 1.508 0.272 \nRFP 1.264 1.364 0.252 \nRrt8-mNG GFP 1.348 1.353 0.242 \nRFP 1.254 1.398 0.206 \nwild type GFP 1.380 1.648 0.266 \nRFP 1.222 1.382 0.194 \n 901 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.25.678471doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}