Single-cell RNA-seq reveals early heterogeneity during ... · 04/09/2020 · 126 divisions during ageing in yeast, and that the division dynamics early in life can predict 127 lifespan

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Single-cell RNA-seq reveals early heterogeneity during ageing 1

in yeast 2

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Yi Zhang1#, Jincheng Wang1#, Yuchen Sang1, Shengxian Jin1, Xuezheng Wang2,3, 4

Gajendra Kumar Azad4,5, Mark A. McCormick6,7, Brian K. Kennedy4,8,9, Qing Li2, Jianbin 5

Wang10, Xiannian Zhang11*, and Yanyi Huang1,12-14* 6

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1 Beijing Advanced Innovation Center for Genomics (ICG), Biomedical Pioneering 8

Innovation Center (BIOPIC), Peking-Tsinghua Center for Life Sciences, School of Life 9

Sciences, Peking University, Beijing 100871, China 10

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2 State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences and 12

Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China 100871 13

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3 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 15

100871 16

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4 Departments of Biochemistry, Yong Loo Lin School of Medicine, National University of 18

Singapore, Singapore, 117596 19

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5 Department of Zoology, Patna University, Patna, Bihar, India, 800005 21

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6 Department of Biochemistry and Molecular Biology, School of Medicine, University of 23

New Mexico Health Sciences Center, Albuquerque, NM, 87131, USA 24

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7 Autophagy Inflammation and Metabolism Center of Biomedical Research Excellence, 26

Albuquerque, NM 87131, USA. 27

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8 Healthy Longevity Programme, Yong Loo Lin School of Medicine, National University of 29

Singapore 30

.CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under apreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in

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9 Centre for Healthy Longevity, National University Health System, Singapore, 119228 32

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10 School of Life Sciences, Beijing Advanced Innovation Center for Structural Biology, 34

Tsinghua University, Beijing 100084, China 35

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11 School of Basic Medical Sciences, Beijing Advanced Innovation Center for Human Brain 37

Protection, Capital Medical University, Beijing 100069, China 38

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12 Analytical Chemistry, College of Chemistry, Peking University, Beijing 100871, China 40

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13 Institute for Cell Analysis, Shenzhen Bay Laboratory, Shenzhen 518132, China 42

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14 Chinese Institute for Brain Research (CIBR), Beijing 102206, China 44

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#These authors contributed equally to this work. 46

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*Corresponding authors: [email protected] (X.Z.), [email protected] (Y.H.) 48

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mailto:[email protected]

mailto:[email protected]

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Abstract 51

The budding yeast Saccharomyces cerevisiae has relatively short lifespan and is 52

genetically tractable, making it a widely used model organism in ageing research. Here, 53

we carried out a systematic and quantitative investigation of yeast ageing with single-cell 54

resolution through transcriptomic sequencing. We optimized a single-cell RNA sequencing 55

(scRNA-seq) protocol to quantitatively study the whole transcriptome profiles of single 56

yeast cells at different ages, finding increased cell-to-cell transcriptional variability during 57

ageing. The single-cell transcriptome analysis also highlighted key biological processes or 58

cellular components, including oxidation-reduction process, oxidative stress response 59

(OSR), translation, ribosome biogenesis and mitochondrion that underlie ageing in yeast. 60

Remarkably, we uncovered a molecular marker, FIT3, that was linked to mitochondrial DNA 61

loss and indicated the early heterogeneity during ageing in yeast. We also analyzed the 62

regulation of transcription factors and further characterized the distinctive temporal 63

regulation of the OSR by YAP1 and proteasome activity by RPN4 during ageing in yeast. 64

Overall, our data profoundly reveal early heterogeneity during ageing in yeast and shed 65

light on the ageing dynamics at the single cell level. 66

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Introduction 68

It has been known for a long time that budding yeast Saccharomyces cerevisiae have 69

limited division potential, only producing a finite number of daughter cells before death1. 70

This phenomenon is defined as replicative ageing, and the number of daughter cells 71

produced before death is defined as the replicative lifespan (RLS)2. Owing to its relatively 72

short lifespan, detailed knowledge of its biology and its easy genetic manipulation, S. 73

cerevisiae is regarded as an ideal model organism to study ageing3. Indeed, many ageing 74

genes and signaling pathways initially found in yeast have also been later found to be 75

conserved in other organisms, such as C. elegans, M. musculus and even humans4. 76

77

A dilemma of replicative ageing research in yeast exists between the rarity of old cells 78

among an exponentially growing population either on a solid agar plate or in liquid media 79

and the large number of pure old cells conventionally required for biochemical, genomic or 80

transcriptomic analysis. To address this problem, several approaches have been 81

developed to enrich old yeast cells, including magnetic sorting, elutriation, genetic 82

programming and even computation5-9. However, these methods have yet to be successful 83

at simultaneously ensuring both the quantity and purity of the isolated old yeast cells much 84

less distinguishing old but living cells from dead ones. In addition, conventional bulk 85

population analysis of ageing yeast cells may likely obscure some specific features within 86

sub-populations due to the average effect10. Recent advances in microfluidics and single-87



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cell imaging have revealed some phenotypic details of replicative ageing in yeast11-14; 88

however, a systematic and quantitative investigation of yeast ageing at the single-cell and 89

transcriptome level would be highly valuable. 90

91

Here, we developed a single-cell RNA-seq approach to study the replicative ageing of 92

yeast and quantitatively assessed the heterogeneity between single yeast cells. Instead of 93

partially purifying millions of old cells, exploiting single-cell technologies enabled us to 94

obtain novel insights into yeast ageing from hundreds of single cells with precise ages. By 95

profiling the transcriptomic landscapes of single yeast cells, we observed an increased 96

cell-to-cell transcriptional variability and identified key functional biological processes or 97

cellular components that were highly enriched during ageing. We also found early 98

heterogeneity during ageing, indicated by a molecular marker of iron transport linked to 99

mitochondrial DNA loss, and successfully characterized the distinctive temporal regulation 100

of transcription between slow-dividing and fast-dividing age subgroups. 101

102

Results 103

Isolation of single yeast cells during ageing and scRNA-seq. Single yeast cells from 104

isogenic populations ultimately have different lifespans. In fact, this is a universal 105

phenomenon of ageing across species, albeit in different forms and ranges. And previous 106

single-cell imaging data of replicative ageing in yeast have provided evidence of such 107

heterogeneity. For example, when re-analyzing the single cell imaging data from the 108

microfluidic-based yeast ageing studies11,12, we can observe that as early as 8 hr after birth, 109

the distribution of generations of single yeast cells had already become dispersed, and the 110

ranges of the distribution gradually increased at 12 hr and 16 hr after birth (Supplementary 111

Fig. 1a), showing that some cells always divided more rapidly than others ever since early 112

in life. These early-stage cell division dynamics in yeast seems closely associated with 113

replicative age, with a positive correlation between the generations at early time points (8hr, 114

12hr, 16hr) after birth and the RLS (R=0.46, 0.64, 0.73; P=9.6x10-5, 7.7x10-9, 7.7x10-9; 115

Supplementary Fig. 1b) at the single-cell level. This new finding is consistent with the 116

previous report that the division time of single yeast cells early in life is negatively correlated 117

with RLS, and the division time increases dramatically when approaching the end of life11. 118

It was also reported previously that early in life, the gene expression level of HSP104, 119

which encodes a molecular chaperone that maintains proteostasis in yeast, negatively 120

correlates with RLS11,12. Accordingly, after re-analyzing the single cell imaging data11,12, we 121

also observed a negative correlation between the generations at early time points during 122

ageing and the HSP104 gene expression level indicated by a GFP tag fused to this gene 123

in single yeast cells (R=-0.43, -0.51, -0.56; P=2.8x10-4, 8.4x10-6, 7.8x10-7; Supplementary 124

Fig. 1c). Collectively, these single-cell imaging data indicate an early heterogeneity of cell 125



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divisions during ageing in yeast, and that the division dynamics early in life can predict 126

lifespan. 127

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To probe more deeply into the mechanisms underlying this early heterogeneity revealed 129

by single-cell imaging, we further developed and applied scRNA-seq for transcriptome 130

profiling of yeast during ageing (Fig. 1a; see Methods). We first conducted an RLS assay 131

by continually performed manual microdissection of single yeasts on a solid agar plate15. 132

At three different time points (2 hr, 16 hr and 36 hr after birth), we manually isolated single 133

ageing yeast cells from the plate and placed the cells individually into a single tube prefilled 134

with lysis buffer containing an external RNA control consortium (ERCC) spike-in for 135

assessing technical noise then followed the Smart-seq2-based protocol16,17 with refined 136

modifications and optimization for yeast ageing research (see Methods). 137

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In total, we collected 136 yeast ageing cells for sequencing. The timepoints of isolation and 139

number of generations at that time were precisely recorded for each cell (Supplementary 140

Table. 1). After filtering out the cells with a low number of genes detected, insufficient read 141

counts and ERCC-dominated samples (Supplementary Fig. 2a-c; see Methods), we finally 142

retained scRNA-seq data of 125 cells composed of 37, 43 and 45 single cells in the 2-hr 143

(young), 16-hr (early age) and 36-hr (late age) age groups, respectively, for further analysis. 144

We also compared our scRNA-seq data to the only 2 available scRNA-seq datasets of S. 145

cerevisiae published recently18,19. Our method yielded, on average, 2,202 genes detected 146

per cell, which is comparable to the dataset from Gasch et al18 (2,202 vs 2,392) with good 147

accuracy and sensitivity, similar to the dataset from Nadal-Ribelles et al19 (Supplementary 148

Fig. 2d-e; Supplementary Table 1). 149

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Cell-to-cell transcriptional variability during ageing in yeast. We sought to explore the 151

cell-to-cell transcriptional variability within different age groups using scRNA-seq data. 152

Overall, we observed increased cell-to-cell transcriptional variability during ageing in yeast 153

based on a correlation analysis in which the transcriptional variability was measured as the 154

biological noise over the technical noise20 (Fig. 1b; see Methods). We verified this increase 155

in cell-to-cell transcriptional variability alternatively using a quantitative statistical method21 156

and respectively identified 145, 312 and 524 highly variable genes (HVGs) with coefficients 157

of variation (CV) that were significantly higher than those of the ERCC spike-in reference 158

within each age group (Supplementary Fig. 3a; see Methods). Interestingly, by Gene 159

Ontology (GO) analysis of these HVGs using DAVID22, the biological processes of cellular 160

iron ion homeostasis and siderophore transport were specifically found to be highly 161

enriched in the 16-hr early age group with high statistical significance, implying an early 162

heterogeneity during ageing in yeast with regard to iron transport (Supplementary Table. 163



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2). 164

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Because all of the ageing single yeast cells analyzed did not have synchronized cell cycles, 166

we wondered whether and to what extent the cell-to-cell transcriptional variability was 167

associated with the cell cycle. We found that 19.3%, 12.8% and 15.5% of HVGs, 168

respectively, among the 3 age groups were regarded as cell-cycle-regulated periodic 169

genes23 (Supplementary Fig. 3b). These results are consistent with a recent report of 170

scRNA-seq in budding yeast that cell-cycle-regulated periodic genes were enriched in 171

HVGs19. However, the trend of increased cell-to-cell transcriptional variability during ageing 172

remained even when these cell-cycle-regulated periodic HVGs were removed from the 3 173

age groups (117, 272 and 443 HVGs remained, respectively; Supplementary Fig. 3b). We 174

further confirmed this trend using principal component analysis (PCA). Regardless of 175

whether the cell-cycle-regulated periodic genes were included in the scRNA-seq dataset 176

used as input for the PCA or not, the 3 age groups were always successfully separated 177

along the axis of first PCA component and were increasingly dispersed (Fig. 1c; 178

Supplementary Fig. 3c); moreover, the top 30 genes based on the absolute loading values 179

for the first PCA component always highly overlapped and were enriched in the biological 180

process of cellular response to oxidative stress, which reflects ageing itself rather than the 181

cell cycle (Supplementary Fig. 3d-e; Supplementary Table. 3). We also performed 182

pseudotime analysis using Monocle24 and found that while the young cells (2-hr) were still 183

very concentrated, the cells of the early age group (16-hr) had already become scattered 184

along the trajectory (Fig. 1d; Supplementary Fig. 3f). 185

186

Global differential gene expression during ageing in yeast. In addition to exploring the 187

cell-to-cell transcriptional variability within different age groups, the scRNA-seq data also 188

allow us to globally investigate the differential gene expression between age groups. Thus, 189

we conducted a pairwise comparison among the 3 age groups using DESeq225 190

(Supplementary Fig. 4a; see Methods). Obviously, more differentially expressed genes 191

were found in the 36-hr late age group compared to the 2-hr group (Supplementary Fig. 192

4a, right panel; Supplementary Table. 4). The biological processes of oxidation-reduction 193

and the oxidative stress response (OSR) were highly enriched in the 36-hr group (75 and 194

26 out of 551 genes, respectively), while translation and ribosome biogenesis were highly 195

enriched in the 2-hr group (50 and 38 out of 138 genes, respectively) based on the GO 196

analysis of biological process using DAVID22 (Fig. 1e, right panel). Moreover, 145 out of 197

551 genes that were highly expressed in the 36-hr late age group compared to the 2-hr 198

group were enriched in mitochondrion as revealed by the GO analysis of cellular 199

components (Fig. 1e, left panel; Supplementary Table. 4). 200

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The average normalized gene expression levels across age groups further demonstrated 202

an age-dependent increase in oxidation-reduction, OSR and mitochondrion as well as a 203

decrease in translation and ribosome biogenesis (Fig. 1f). Indeed, these transcriptome 204

changes had already occurred in the 16-hr early age group. Although far fewer differentially 205

expressed genes were found in the 16-hr early age group compared to the 2-hr group 206

(Supplementary Fig. 4a, left panel), early signs of upregulation in oxidation-reduction and 207

downregulation in ribosome biogenesis (15 out of 108 genes and 4 out of 10 genes, 208

respectively) were observed (Supplementary Fig. 4b; Supplementary Table 4) . Notably, 209

the global differentially expressed genes between age groups and their enriched GO 210

categories from our scRNA-seq data were found to coincide well with a recent report of 211

transcriptome changes during ageing in yeast9 and were even partially consistent with 212

another proteome analysis of ageing in C. elegans26, although they were both based on 213

bulk population analysis. These ageing associated GO categories analyzed by DAVID 214

were also confirmed by ClusterProfiler27 (Supplementary Fig. 5a-f). 215

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Weighted gene co-expression network analysis during ageing in yeast. To find the 217

clusters of highly correlated genes during ageing in yeast, we performed a weighted gene 218

co-expression network analysis (WGCNA)28-29, and generated 7 different gene co-219

expression modules (Fig. 2a; see Methods). Among these gene co-expression modules 220

(Fig. 2b-d), we further identified 52 hub genes from 731 genes in the positively correlated 221

modules which were upregulated during ageing (Supplementary Table. 5). These genes 222

are mainly enriched in OSR and oxidation-reduction process by GO analysis using 223

Metascape30, and 5 of them are even involved in the longevity regulatory pathways, 224

including HSP104, which is a molecular marker of ageing in yeast identified previously11,12 225

(Supplementary Fig. 6a; Supplementary Table. 5). 70 hub genes were identified from 410 226

genes in the negatively correlated modules which were downregulated during ageing and 227

they are mainly enriched in ribosome biogenesis (Supplementary Fig. 6b). All these 228

findings echo well the results of previous global differential gene expression analysis during 229

ageing in yeast. 230

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Differential gene expression between slow- and fast-dividing age subgroups. The 232

number of genes detected per cell within age groups was found to be positively correlated 233

with the generation, suggesting another facet to understand the heterogeneity of cell 234

divisions during ageing in yeast, and the 16-hr and 36-hr age groups were thus split by 235

their respective mean generation into slow-dividing (16-hr/S, 36-hr/S) and fast-dividing (16-236

hr/F, 36-hr/F) subgroups (Fig. 3a, b; Supplementary Table. 1). Comparing the early age 237

subgroups of 16-hr/S and 16-hr/F by DESeq225 with stringent statistical filtering yielded 29 238

differentially expressed genes, with 5 highly expressed and 24 weakly expressed in 16-239

hr/S (Fig. 3c; Supplementary Table. 6). FIT3 and HAC1 are highly expressed in 16-hr/S. 240



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FIT3, together with FIT2 and FIT1, as facilitators of iron transport in yeast, encodes a cell 241

wall mannoprotein31. These genes were reported to be induced upon iron deprivation or 242

mitochondrial DNA loss32,33. HAC1 is a transcription factor that regulates the unfolded 243

protein response (UPR), and interestingly, one of its regulatory targets is FIT334,35. Indeed, 244

FIT3 and HAC1 were not only highly expressed in 16-hr/S but also in 36-hr/S (Fig. 3d, e). 245

Moreover, the gene expression of FIT3 and HAC1 negatively correlated with the age of 246

single cells in the 16-hr age group (R=-0.55, -0.38; P=1.3 x 10-4, 1.5 x 10-2) as well as the 247

36-hr group (R=-0.62, -0.44; P=5.6 x 10-6, 2.2 x 10-3; Fig. 3f; Supplementary Fig. 7a; 248

Supplementary Table. 6). Surprisingly, gene expression levels of several other iron 249

transporters, including FIT2 and FET331, were also found to be negatively correlated with 250

the generation of single cells in the 16-hr and 36-hr age groups (Supplementary Fig. 7b, c; 251

Supplementary Table. 6). Finally, as single-gene deletions of FIT2 and FET3 were both 252

reported to extend the lifespan in yeast4, we measured the RLS of yeast after deleting FIT3, 253

and verified that this strain is long-lived as well (Fig. 3g). Collectively, these results clearly 254

reveal a molecular marker of iron transport that can quantitatively indicate early 255

heterogeneity during ageing in yeast, which might be mediated by mitochondrial DNA 256

loss33. This early ageing transcriptional signature can last until an advanced age and 257

predict the lifespan. 258

259

Interestingly, we also revealed that 11 out of 24 genes expressed at low levels in 16-hr/S 260

were enriched in mitochondrion, and these genes were also expressed at lower levels in 261

36-hr/S than in 36-hr/F (Fig. 3c-e; Supplementary Table. 6). This further suggests a 262

relatively poor mitochondrial function in the slow-dividing cells. Among these 11 weakly 263

expressed mitochondrial genes (Fig. 3c), COR1 is the core subunit of ubiquinol-264

cytochrome c reductase which belongs to complexes III and COX4 is an important 265

component of cytochrome c oxidase which belongs to complexes IV of the mitochondrial 266

inner membrane electron transport chain. It has been reported that mutation of either 267

COR1 or COX4 can cause a decrease in respiration, slow cell growth and even a shorter 268

lifespan34-38. These 11 mitochondrial genes showed no overlap with the 145 mitochondrial 269

genes that were globally upregulated during ageing (Fig. 1e and Fig. 3c, Supplementary 270

Table. 4 and 6); in contrast, no significant differential expression of those 145 mitochondrial 271

genes was observed between these two subgroups (Fig. 3e). These results successfully 272

characterize divergent mitochondrial gene expression profiles between age groups and 273

subgroups that would be masked in the bulk population analysis but can be identified by 274

scRNA-seq. 275

276

The correlation analysis between the gene expression and the generation of single cells 277

also resulted in genes that were positively correlated with generation in the 16-hr early age 278

group are enriched in ribosome biogenesis (Supplementary Fig. 7d; Supplementary Table. 279



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6). This suggests a downregulation of at least some ribosome biogenesis genes during 280

early ageing and it is mainly contributed by the cells from the slow-dividing age subgroup, 281

which are inclined to be short-lived (Supplementary Fig. 7e). Meanwhile, genes enriched 282

in translation, mitochondrial translation and glycolytic processes were positively correlated 283

with generation in the 36-hr late age group (Supplementary Fig. 7f). This agrees with the 284

differential gene expression analysis above, suggesting a relatively poor machinery of 285

translation and mitochondrion in the slow-dividing age subgroups. In summary, all these 286

results thoroughly characterize early and late heterogeneity during ageing in yeast at the 287

single-cell transcriptome level. 288

289

Temporal regulation of transcription factor (TF) between age subgroups. We further 290

investigated the regulatory variation in transcription factors (TFs) between age subgroups, 291

analyzing 634 overlapping TF targets (gene clusters) based on reported studies on 292

budding yeast18,39-43. To eliminate false positives, we performed stringent statistical 293

analysis with three approaches (see Methods). First, we conventionally compared the 294

median TF target expressions between age subgroups. This led to 16 TF targets that were 295

significantly activated in the 16-hr/F subgroups and 11 TF targets in 36-hr/F compared to 296

their counterparts, respectively (Supplementary Fig. 8a, b; Supplementary Table. 7). Then, 297

we ran a Wilcoxon rank sum test comparing normalized gene expression levels of each 298

set of TF targets to that of all other detected genes for each cell, taking P < 0.0001 as the 299

criterion, followed by intersection with TF targets derived from the conventional analysis. 300

This led to 5 and 2 TF targets that were significantly activated in 16-hr/F and 36-hr/F, 301

respectively (Fig. 4a; Supplementary Fig. 8c; Supplementary Table. 7). Subsequently, we 302

employed correlation analysis between TF target expression and the generation of single 303

cells in the 16-hr and 36-hr age groups, taking P < 0.05 as the criterion (Supplementary 304

Fig. 9a, b; Supplementary Table. 7), followed by intersection with TF targets derived from 305

the former two approaches. 306

307

Finally, YAP1 was found to be most significantly active in regulating the early age subgroup 308

of 16-hr/F compared to 16-hr/S (Fig. 4b, c), although the other 4 TFs of ABF1, REB1, INO4 309

and TYE7 demonstrated a similar trend with less statistical significance (Supplementary 310

Fig. 8d, e). Moreover, 2 TF targets of RPN4 were found to be most highly regulated at 36-311

hr/F compared to 36-hr/S (Fig. 4b, c). YAP1 is involved in activating the transcription of 312

antioxidant genes in response to oxidative stress44,45. The relatively high activation of YAP1 313

targets in the 16-hr/F early age subgroup suggests that the rapidly dividing single cells, 314

which are inclined to be long-lived, may have a better defence system against oxidative 315

stress than the slow-dividing cells. RPN4 is a TF that stimulates proteasome biogenesis 316

for the degradation of damaged proteins46. The relatively high activation of RPN4 targets 317

in the 36-hr/F late age but rapidly dividing subgroup supports the idea that proteasome 318



https://www.yeastgenome.org/go/3700

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capacity is critical to maintain the vigour and proteostasis of yeast cells, especially when 319

approaching the end of life, as elevated RPN4 expression is essential for extending the 320

RLS in yeast47. Altogether, these findings reveal early and late heterogeneity by distinctive 321

temporal regulation of TFs during ageing in yeast, and combined with the aforementioned 322

differential gene expression analysis between age groups and subgroups, we successfully 323

depicted a landscape of ageing in yeast with unprecedented detail at single-cell resolution. 324

Discussion 325

Although transcriptome changes during ageing in yeast based on bulk population analyses 326

have been reported8,9, such analyses at the single-cell level had not yet been performed. 327

Here, we first identified an early heterogeneity of cell divisions during ageing in yeast by 328

single-cell imaging and then developed and applied scRNA-seq for single-cell 329

transcriptome analysis during ageing in yeast for the first time. 330

331

Using scRNA-seq technology, we overcame the difficulty of purifying the large number of 332

old cells required for conventional transcriptome analysis during ageing in yeast. More 333

importantly, by single-cell transcriptome analysis, we not only successfully recapitulated 334

the results of the bulk population analysis but also teased out specific transcriptional 335

features at the single-cell resolution that would otherwise be masked in a bulk population. 336

For example, by scRNA-seq we revealed that while globally there were an age-dependent 337

upregulation of many mitochondrial genes between age groups, a small number of different 338

but important mitochondrial genes were significantly downregulated in the slow-dividing 339

age subgroups compared to their fast-dividing counterparts. This provides novel and 340

unprecedented insights into our understanding of the ageing process. Our results have 341

unveiled the increased cell-to-cell transcriptional variability independent of the cell cycle 342

and identified an early heterogeneity during ageing in yeast. This also coincides with recent 343

reports of scRNA-seq in mouse immune cells and human pancreatic cells during 344

ageing48,20. 345

346

By single-cell transcriptome analysis, we also identified a new molecular marker of iron 347

transport that both indicates early heterogeneity during ageing in yeast and predicts 348

lifespan. Remarkably, FIT3 together with several other iron transporter genes, such as FIT2 349

and FET3, had a negative correlation with the age of single yeast cells from both early and 350

late timepoints. These genes are known to be induced upon iron deprivation or 351

mitochondrial DNA loss32,33. Moreover, these genes can all extend the RLS in yeast when 352

deleted4 (Fig. 3g). Therefore, we propose a model in which early heterogeneity during 353

ageing in yeast is associated with differential mitochondrial dysfunction that affects and is 354

mediated by iron transport. And this model is partially supported by a report published 355



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recently, showing age-dependent heterogeneity via a FIT2 reporter that is correlated with 356

vacuolar pH, mitochondrial function and lifespan in sub-populations of yeast cells50. More 357

evidence may be needed to further validate this model, and presently it remains 358

challenging to disentangle the cause-effect relationships between mitochondrial 359

dysfunction and early heterogeneity during ageing. However, we keep optimistic that these 360

problems can be solved if the potential of modern single-cell technologies integrated with 361

other new methods are fully employed. 362

363

Based on the scRNA-seq data and knowledge of TF targets in the budding yeast 364

Saccharomyces cerevisiae18,39-43, we also explored TF regulation at the single cell level 365

and found distinctive temporal regulation of TFs during ageing in yeast. YAP1 is a key TF 366

responding to oxidative stress44,45. While it was highly activated in 16-hr/F compared to 16-367

hr/S early age subgroup, no significant difference of its activities were observed between 368

36-hr/F and 36-hr/S late age subgroups (Fig. 4b, c), implicating its vital role during early 369

ageing, which in turn affects overall lifespan. In contrast, RPN4, the TF essential for 370

proteasome biogenesis and RLS extension46,47, was only prominently activated in 36-hr/F 371

compared to 36-hr/S late age subgroup, suggesting a dramatic loss of proteostasis in the 372

late age and slow-dividing subgroup49 (Fig. 4b, c; Supplementary Fig. 8a-c; Supplementary 373

Fig. 9a, b). These findings point not only to early but also late heterogeneity during ageing 374

in yeast, and provide novel insights into understanding the molecular mechanisms of 375

ageing that will lead to therapeutics for healthy ageing in humans ultimately51. 376

377

Acknowledgement 378

We thank the BIOPIC sequencing platform at Peking University for the assistance of high-379

throughput sequencing experiments. This work was supported by Ministry of Science and 380

Technology of China (2018YFA0108100), National Natural Science Foundation of China 381

(21917802, 21525521), 2018 Beijing Brain Initiative (Z181100001518004), and Beijing 382

Advanced Innovation Center for Genomics. 383

384

Author contributions 385

Y.Z. and Y.H. conceived and designed the project. Y.Z., J.W., Y.S., S.J., X.Z., and G.K.A 386

conducted the experiments. Y.Z., J.W., B.K., Q.L., J.W., X.Z. and Y.H. analyzed the data. 387

Y.Z., J.W., B.K., X.Z., and Y.H. wrote the manuscript with the help from all other authors. 388

389



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Conflict of interest statement 390

The authors declare no conflict of interest. 391

392



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Methods 393

Strains and growth conditions. WT Saccharomyces cerevisiae in both BY4741 and 394

BY4742 backgrounds were used for single-cell imaging analysis. The strain of Hsp104-395

GFP was derived from the standard GFP strain library in WT BY4741 background. WT 396

BY4742 background was used in scRNA-seq during aging. WT BY4741 background was 397

used in the replicative lifespan assay of FIT3Δ. For single-cell imaging, the cells were 398

grown in the YPD liquid media before and after loading into the microfluidic chips. For 399

scRNA-seq during aging and replicative lifespan assay of FIT3Δ, the cells were grown on 400

SD solid agar plates. 401

402

Single-cell imaging data analysis. The approach for single-cell imaging data analysis 403

has been reported in detail elsewhere11. Yeast cell culture was grown in YPED at 30°C with 404

OD600 of 0.5 before loading into the microfluidic device by a syringe connected to an 405

automatically controlled peristaltic pump. The microfluidic device was mounted on a Nikon 406

TE2000 time-lapsed microscope by a customized holder. Bright field images were taken 407

once every 10 minutes throughout the whole life, and fluorescent images were taken once 408

every 2 hours or 4 hours for measuring the HSP104-GFP level. The images were 409

processed by ImageJ and MATLAB. 410

411

Dissection and isolation of single cells for RNA-seq. We first inoculated WT yeast cells 412

onto a solid agar plate with SD media and followed a standard protocol of replicative 413

lifespan assay by continual (no storage in the 4°C fridge overnight) manual 414

microdissection15. At 3 time points ( 2hr, 16hr and 36hr after birth), single yeast aging cells 415

from the plate were manually dissected and placed individually into a single tube prefilled 416

with lysis buffer containing zymolyase (3 x 10-2 U/µl) for efficiently digesting the cell wall 417

and external RNA control consortium (ERCC) spike-in (8000 molecules) for assessing 418

technical noise, followed by immediate freeze in liquid nitrogen and then storage in a -80°C 419

freezer. 420

421

Library preparation for scRNA-seq . After collecting all the single yeast aging cells, we 422

performed scRNA-seq based on Smart-seq216,17 with fine optimization. To efficiently lyse 423

the single yeast aging cell and avoid possible mRNA degradation, we vigorously vortexed 424

the lysis tubes for 1 min in a cold room. Then we kept the lysis tubes at 30°C for 10 min, 425

followed by 3 min at 72°C. Subsequently, we added the RT reaction mix (RT-buffer and 426

Invitrogen SuperScript II ) for reverse transcription. Reverse transcription was carried out 427

at 42°C for 90 min first, followed by 12 rounds of temperature cycling between 50°C and 428

42°C with 2 min each. The reaction was heat inactivated at 70°C for 15 min and then cooled 429



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down to 4°C. The oligo-dT and TSO primers used here were biotinylated to avoid potential 430

production of excessive primer dimers and concatamers. After RT, the cDNA were 431

amplified between 20~25 cycles using KAPA HiFi enzyme. After cDNA amplification, the 432

samples were purified using Agencourt AMPure XP beads at 0.8X bead concentration and 433

quantified using Qubit Hs Assay (Life Technologies). We also checked the samples by a 434

fragment analyzer to confirm the clean peak at ~1.7 kb before subsequent processing. 1~2 435

ng of cDNA was subjected to a tagmentation-based protocol (Vazyme TruePrep Kit) with 436

10 min at 55°C and dual index amplification for the library with 8~12 cycles. The final 437

libraries were purified twice using AMPure XP beads at 0.8X bead concentration and 438

resuspended in 15~20 µl elution buffer. Libraries were then quantified using Qubit Hs 439

Assay before pooling for sequencing. Sequencing was performed in paired-end mode 440

using Illumina NextSeq. 441

442

scRNA-seq data pre-processing and filtering. Paired-end reads were mapped to the 443

S288c Saccharomyces cerevisiae genome R64 version (www.yeastgenome.org) with 444

ERCC spike-in sequences added using HISAT2 (version 2.1.0). Resulting bam files were 445

sorted and indexed using samtools (version 1.1). Final read counts mapped to genes were 446

extracted using FeatureCounts. Sequenced single yeast aging cells were removed from 447

the analysis if they have < 1000 genes detected and 40,000 total mapped reads per cell, 448

or if the proportion of ERCC spike-ins to total-mapped reads was > 0.74. After filtering, a 449

scRNA-seq data set with 125 single yeast aging cells was used for the subsequent analysis. 450

451

Normalization. Unless noted, normalization of raw read counts was done using the 452

DESeq225 package (v.1.22.2) in R. The size factor was computed by a formula embedded 453

in DESeq2 for each cell based on the raw read counts matrix of all samples. Then these 454

size factors were applied for normalizing different cells and finally the gene expression 455

values are presented in the log2 space (log2NormCounts). 456

457

Estimation of cell-to-cell transcriptional variability and identification of highly 458

variable genes. We used two methods to estimate the cell-to-cell transcriptional variability 459

during aging in yeast. The first was a correlation based method modified from Enge, M. et 460

al20, where the transcriptional noise was expressed as biological variation over technical 461

variation. First, we calculated the biological variation bij = 1-cor(xij, ui), where ui was the 462

mean gene expression vector for the single cells in age group of i (2hr, 16hr and 36hr), and 463

xij was the gene expression vector of cell j in the age group of i. Next, we calculated the 464

corresponding technical variation tij = 1-cor(xcontrij, ucontr) where xcontr

ij and ucontr are the 465

expression vector and mean expression vector of the ERCC spike-in controls. Finally the 466

measurement of bij/tij which reflected the biological noise as a fraction of technical noise 467



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for each cell was used for boxplot across different age groups as shown in Fig.1b. The 468

second method was based on quantitative statistics reported previously21 (see 469

Supplementary Note 6 of Brennecke et al21 for details of the statistical model). Briefly, to 470

infer the genes that were highly variable within each age group, a linear regression model 471

was applied to fit the relationship between the squared coefficient of variation (CV2) and 472

the mean expression of ERCC spike-ins, and only genes with biological squared coefficient 473

of variation > 0.25 (CV2 > 0.25) and FDR< 0.1 after multiple testing correction were 474

regarded as HVGs. 475

476

Differential gene expression and GO analysis. The differential gene expression analysis 477

between pairwise age groups and subgroups was based on DESeq225 with default 478

parameters, taking log2FC >1 and adjusted P value < 0.05 as significant. GO analysis of 479

these differentially expressed genes was performed by functional annotation tool of 480

DAVID22 that classify the ontology of each gene into biological process or cellular 481

component. The GO term enrichment results derived from DAVID were further verified 482

alternatively by the R package of ClusterProfiler27. 483

484

Weighted gene co-expression network analysis. WGCNA28-29 was performed on 485

normalized gene expression data from DESeq225, using 2498 genes, which are selected 486

by removing unclassified genes (grey module) from the first round of WGCNA28-29. Then 487

the second round WGCNA29-30 was performed following the standard process. Briefly, the 488

topological overlap matrix (TOM) was constructed with a soft Power and was set to 2. The 489

hub genes for each module were identified as module membership based group trait > 490

0.65 and gene significance > 0.2. 491

492

Statistical analysis of regulation of transcription factor between age subgroups. To 493

identify transcription factors with distinct regulation between age subgroups, 3 statistical 494

approaches were applied stringently. The first one was to conventionally comparison of TF 495

targets expression between age subgroups. The TF targets expression was defined as the 496

averaged normalized expression of each set of TF targets for each cell. And we took log2FC 497

(FoldChange) of median TF targets expression between age subgroups >1 (log2FC > 1) 498

and a welch t test P value < 0.01 as significant, which resulted in 16 and 11 TF targets 499

respectively that were significantly activated in the age subgroups of 16-hr/F and 36-hr/F 500

compared to their counterparts (Supplementary Fig. 8a, b; Supplementary Table. 6). The 501

second one was to further run a Wilcoxon rank sum test for each single cell that compare 502

internally the normalized gene expression levels of each set of TF targets to all other 503

detected genes for that cell, taking P < 0.0001 as criterion (indicated as regulon activity 504

“on” ), followed by intersection with TF targets derived from the first approach. This 505



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approach was similar with that from Gasch et al18. The last one was to correlate the TF 506

targets expression with the generation of single cells in the age groups of 16-hr and 36-hr 507

respectively, taking P < 0.05 as criterion, followed by intersection with TF targets derived 508

from the former two approaches to avoid potential false positive results. 509

510

PCA analysis. Raw read counts matrix with or without cell-cycle-regulated periodic 511

genes23 were used as inputs for PCA by Seurat52. When the cell-cycle-regulated periodic 512

genes were included, Seurat generates 631 common variable genes of all 125 single yeast 513

aging cells, whose normalized read counts are further applied for PCA. When the cell-514

cycle-regulated periodic genes were excluded, Seurat generated 599 common variable 515

genes of all 125 single yeast aging cells for PCA. 516

517

Data availability 518

scRNA-seq data generated in this study has been uploaded to Gene Expression Omnibus 519

under accession number xxxxxx. 520

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Figure legends 630

Fig.1 | Cell-to-cell transcriptional variability and global differential gene expression 631

during ageing in yeast. a, Schematic of the workflow of scRNA-seq during aging in yeast. 632

Each single yeast ageing cell (indicated as gray ellipse in the dashed area) was manually 633

isolated at 2-hr, 16-hr or 36-hr after birth, and then placed individually into a single tube 634

prefilled with lysis buffer, followed by modified and optimized Smart-seq216,17. b, Boxplot 635

showing an increased cell-to-cell transcriptional variability during aging in yeast based on 636

a correlation analysis where the transcriptional variability was measured as biological noise 637

over the technical noise (see Methods). Boxes indicated the first and third quartiles, 638

separated by median line. Whiskers indicated last values within 1.5 x the interquartile range 639

for the box; Wilcoxon P values were also shown. c, PCA plot of single cells (n=125) from 640

different age groups (no cell-cycle-regulated periodic genes included as input for PCA). 641

The distribution of single yeast aging cells in the 36-hr late age group was more scattered 642

than that of 2-hr age group and 16-hr early age group, which reflected an increased cell-643

to-cell transcriptional variability. d, Pseudotime trajectory of single cells (n=125) from 644

different age groups (no cell-cycle-regulated periodic genes included as input). The 645

youngest 2-hr age group was very concentrated, whereas the 16-hr early age group and 646

36-hr late age group were very scattered. e, (left) Heatmap of normalized gene expression 647

of 551 upregulated and 138 downregulated genes in the 36-hr age group compared to 2-648

hr age group, across different age groups. The purple bar indicated 145 mitochondrial 649

genes that were highly expressed in the 36-hr late age group. (right) Significance of GO 650

terms of biological processes (BP) in upregulated and downregulated genes respectively 651

(-log10P). f, Boxplot of the average normalized expression of significantly upregulated and 652

downregulated gene categories identified in e, across different age groups. Each black dot 653

in f represented a single cell. **p < 5.5 x 10-7, ***p < 4.2 x 10-9, ****p < 1.6 x 10-13, from 654

Wilcoxon rank sum test. 655

656

Fig.2 | Weighted gene co-expression network analysis during ageing in yeast. a, 657

Dendrogram showing the gene co-expression network constructed using WGCNA. The 658

color bar labeled as “Module” beneath the dendrogram represents the module assignment 659

of each gene. We totally identified 7 modules. b, Module-trait relationship shows that the 660

turquoise module is most positively while the blue module is most negatively correlated 661

with the traits of Group and Generation of the single yeast cells. The upper number within 662

cell represents correlation coefficient and number within brackets refers to the p-value. c 663

and d, Heatmap and barplot showing genes in the turquoise module are upregulated 664

while genes in the blue module are downregulated during ageing in yeast. The rows of 665

heatmap represent gene expression within the corresponding module. The columns of 666

heatmap and barplot refer to the sample. 667

668

Fig.3 | Differential gene expression between slow- and fast-dividing age subgroups. 669

a, (left) Correlation of the number of genes detected and the generation of single cells in 670



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the 16-hr early age group. Each red dot represented a single cell with the number of genes 671

detected and its generation at 16 hr. Blue line was a linear fit with gray area indicating 0.95 672

confidence interval; correlation coefficient (R) and P value (P) were also shown. The 673

dashed line indicated the mean generation. The plot showed a positive correlation between 674

the number of genes detected and the generation at 16 hr among individual cells. (right) 675

Boxplot of generation between early age subgroups 16-hr/S and 16-hr/F that were split by 676

the mean generation of 16-hr early age group; Wilcoxon P value was shown. b, (left) 677

Correlation of the number of genes detected and the generation of single cells at 36-hr late 678

age group and (right) Boxplot of generation between late age subgroups 36-hr/S and 36-679

hr/F that were split by the mean generation of 36-hr late age group, plotted same as in a. 680

Note: The cells with the number of genes below 1000 plotted in both a and b were 681

discarded in the rest analysis. c, Differential gene expression analysis between early age 682

subgroups 16-hr/S and 16-hr/F. The heatmap showed normalized gene expression of 683

statistically significant (Log2|FC|>1 and Padj<0.05) upregulated and downregulated genes 684

in early age subgroup 16-hr/S compared to 16-hr/F, across different age subgroups. d, 685

Boxplots of normalized expression of significant differentially expressed genes of FIT3, 686

HAC1, and gene category of mitochondrion identified in c across different age groups. e, 687

Boxplots of normalized expression of significant differentially expressed genes of FIT3, 688

HAC1, and gene categories of mitochondrion respectively identified in c and Fig. 1e across 689

different age subgroups. Each black dot in d and e represented a single cell. *p and **p < 690

0.05, ***p < 0.01, ****p < 6.1 x 10-5, and “ns” means not significant, from Wilcoxon rank 691

sum test. f, Correlation of normalized gene expression of FIT3 and the generation of single 692

cells in the 16-hr early age group and 36-hr late age group, respectively. Each red dot 693

represented a single cell. Blue line was a linear fit with gray area indicating 0.95 confidence 694

interval; correlation coefficient (R) and P value (P) were also shown. The plot showed a 695

negative correlation for both age groups. g, Survival curve of WT and FIT3Δ. The number 696

in the parenthesis represented the mean RLS and “n” indicated the number of cells 697

assayed for RLS of each strain. 698

699

Fig.4 | Temporal regulation of transcription factor (TF) between age subgroups. a, 700

Heatmap showing differential expression of 5 transcription factor targets in the early age 701

subgroup of 16-hr/F compared to 16-hr/S, and 2 transcription factor targets in the late age 702

subgroup of 36-hr/F compared to 36-hr/S, based on first two statistical criteria (see 703

Methods). b and c, Boxplots of differential expression of YAP1 targets that were highly 704

expressed in the early age subgroup of 16-hr/F compared to 16-hr/S, and 2 RPN4 targets 705

that were highly expressed in the late age subgroup of 36-hr/F compared to 36-hr/S 706

identified by 3 stringent statistical approaches (see Methods), across different age groups 707

and subgroups, respectively. Each black dot in b and c represented a single cell. *p < 708

0.05, **p < 0.01, ***p < 1 x 10-3 , ****p < 1 x 10-4, and “ns” means not significant, from 709

Wilcoxon rank sum test. 710

711

Supplementary Fig.1 | Early heterogeneity of cell divisions during ageing in yeast. a, 712



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The distribution of generation at 8 hr, 12 hr and 16 hr after birth of single yeasts respectively 713

(n=67). The plot showed the heterogeneity of cell divisions occurs early during ageing in 714

yeast as indicated by the mean (Mean) and standard deviation (Std) of generation in the 715

figure. b, The lifespan was plotted against the generation at 8 hr, 12 hr and 16 hr after birth 716

of single yeasts respectively. It show a positive correlation. c, The HSP104-GFP level was 717

plotted against the generation at 8 hr, 12 hr and 16 hr after birth of single yeasts respectively. 718

It show a negative correlation. Each red dot in b or c represented a single cell with its 719

generation and its final lifespan or HSP104-GFP level, while blue line was a linear fit with 720

gray area indicating 0.95 confidence interval; correlation coefficient (R) and P value (P) 721

were also shown. 722

723

Supplementary Fig.2 | Data filtering and technical assessment of scRNA-seq. a, The 724

number of raw read counts plotted against the number of genes detected per cell between 725

different age groups. b, The ERCC ratio plotted against the number of genes detected per 726

cell between different age groups. c, The ERCC ratio plotted against the number of raw 727

read counts per cell between different age groups. Each dot in a-c represented a single 728

cell with color indicating the age group or filtering status it belonged to (n=136 cells). d and 729

e were mean normalized read counts and detection rate (the probability to have a read 730

count number more than 0) plotted against the absolute number of RNA molecules per cell 731

for each of the 92 ERCC RNA spike-in across all the single yeast aging cells that were 732

filtered (n=125 cells). 733

734

Supplementary Fig.3 | Identification of HVGs within different age groups with or 735

without cell-cycle-regulated periodic genes. a, Squared coefficients of variation were 736

plotted against the average normalized read counts for each cell within different age groups 737

with cell-cycle-regulated periodic genes included. A gene was considered as HVG if the 738

coefficient of biological variation was more than 0.5 (with the false discovery rate of 0.1). 739

Red line represented the technical noise fit estimated by the ERCC spike-in RNA21 (see 740

Methods). Endogenous genes, ERCC and HVGs were shown in black, green and magenta 741

dots respectively. b, Venn diagrams of HVGs within different age groups and the putative 742

cell-cycle-regulated periodic genes. The increased cell-to-cell transcriptional variability 743

during ageing still existed even excluding these cell-cycle-regulated periodic HVGs from 3 744

age groups. c, PCA plot of single cells (n=125) from different age groups (with cell-cycle-745

regulated periodic genes included as input for PCA). The 3 age groups were segregated 746

along the first PCA component successfully. d, Visualized plots of top 30 genes by absolute 747

loading values for the first PCA component, with or without cell-cycle-regulated periodic 748

genes included as input for PCA. e, Venn diagrams of the genes with top 30 genes by 749

absolute loading values for the first PCA component, with or without cell-cycle-regulated 750

periodic genes included as input for PCA, overlapped with putative cell-cycle-regulated 751

periodic genes. f, Pseudotime trajectory of single cells (n=125) from different age groups 752

(with cell-cycle-regulated periodic genes included as input). The youngest 2-hr age group 753

was very concentrated, whereas the 16-hr early age group and 36-hr late age group were 754



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23

very scattered in order. 755

756

Supplementary Fig.4 | Differential gene expression between age groups. a, Volcano 757

plot of global differential gene expression analysis between different age groups using 758

DESeq2 (see Methods). The criteria for statistical significance were log2 foldchange of 759

absolute normalized gene expression more than 1 (Log2|FC|>1) and adjusted P value less 760

than 0.05 (Padj<0.05). b, Boxplots of the average normalized expression of typical 761

upregulated and downregulated gene categories identified in the early age group of 16hr 762

compared to 2hr, across different age groups. Each black dot in b represented a single cell. 763

*p < 0.05 and ****p < 1.4 x 10-10 from Wilcoxon rank sum test. 764

765

Supplementary Fig.5 | GO enrichment analysis between age groups. a-f were GO 766

enrichment analysis of differentially expressed genes from the pairwise comparison of 3 767

age groups using the R package clusterProfiler27 (see Methods). The number of genes in 768

the enriched GO category was indicated by the size of the dot while the adjusted P value 769

was indicated by the color of the dot. 770

771

Supplementary Fig.6 | GO analysis of hub genes of ageing related co-expression 772

gene module identified by WGCNA. a, GO terms of 52 hub genes of turquoise module. 773

These hub genes were upregulated during ageing in yeast and are mainly enriched in OSR, 774

oxidation-reduction process and even longevity regulating pathway. b, GO terms of 70 hub 775

genes of blue module. These hub genes were downregulated during ageing in yeast and 776

are mainly enriched in ribosome biogenesis. 777

778

Supplementary Fig.7 | Correlation of gene expression and the generation of single 779

cells in the early and late age groups. a-c, Normalized gene expression of HAC1, FET3 780

and FIT2 plotted against the generation of single cells in the 16-hr early age group and 36-781

hr late age group, respectively. Each red dot represented a single cell with the respective 782

normalized gene expression and its generation, while blue line was a linear fit with gray 783

area indicating 0.95 confidence interval; correlation coefficient (R) and P value (P) were 784

also shown. They all showed negative correlation with statistical significance (P<0.05) 785

except the FIT2 at 16hr (P=0.14). d, Pearson correlation of normalized gene expression 786

with the generation of single cells in the early age group of 16hr, taking P<0.05 as 787

significant. The biological process of iron transport was enriched as negatively correlated 788

while the ribosome biogenesis positively correlated. e, Boxplots of the average normalized 789

expression of gene category of ribosome biogenesis identified in d, across different age 790

groups and subgroups. Each black dot in e represented a single cell. ***p < 2.7 x 10-4, ****p 791

< 3.3 x 10-6, and “ns” means not significant, from Wilcoxon rank sum test. f, Pearson 792

correlation of gene expression with the generation of single cells in the 36-hr late age group, 793

taking P<0.05 as significant. The biological process of iron transport was enriched as 794



https://doi.org/10.1101/2020.09.04.282525


24

negatively correlated while the translation, mitochondrial translation and glycolytic process 795

positively correlated. 796

797

Supplementary Fig.8 | Distinct regulation of TF between age subgroups. a, 16 TF 798

targets that were significantly activated in the early age subgroup of 16-hr/F by 799

conventional comparison of median TF targets expressions to 16-hr/S, taking Log2FC>1 800

and P <0.01 as significant. b, 11 TF targets that were significantly activated in the late age 801

subgroup of 36-hr/F by conventional comparison of median TF targets expressions to 36-802

hr/S, taking Log2FC>1 and P <0.01 as significant. c, The significantly activated TF targets 803

in 16-hr/F and 36-hr/F in contrast to their counterparts were further narrowed down to 5 804

and 2 (highlighted in red in a and b) respectively by Wilcoxon rank sum test comparing 805

normalized gene expression levels of each set of TF targets to that of all other detected 806

genes for each cell, taking P < 0.0001 as the criterion and indicated as “on” of the regulon 807

activity. d and e are boxplots of differential expression of 4 other TF targets that were highly 808

expressed in the early age subgroup of 16-hr/F compared to 16-hr/S identified by the first 809

two stringent statistical approaches (see Methods), across different age groups and 810

subgroups, respectively. Each black dot in d and e represented a single cell. *p < 0.05, **p 811

< 0.01, ***p < 2.8 x 10-4 , ****p < 7.1 x 10-5, and “ns” means not significant, from Wilcoxon 812

rank sum test. 813

814

Supplementary Fig.9 | Correlation of TF targets expression with the generation of 815

single cells in the early and late age groups. a, Pearson correlation of median TF targets 816

expression with the generation of single cells in the 16-hr early age group, taking P<0.05 817

as significant. The expression of YAP1 targets was found to be most positively correlated. 818

b, Pearson correlation of median TF targets expression with the generation of single cells 819

in the 36-hr late age group, take P<0.05 as significant. The expression of 2 RPN4 targets 820

identified by previous two statistical approaches also positively correlated with the 821

generation of single cells in the 36-hr late age group. 822



https://doi.org/10.1101/2020.09.04.282525


cytoplasmic translation

d

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https://doi.org/10.1101/2020.09.04.282525


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https://doi.org/10.1101/2020.09.04.282525


a b

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https://doi.org/10.1101/2020.09.04.282525


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Single-cell RNA-seq reveals early heterogeneity during ... · 04/09/2020 · 126 divisions during ageing in yeast, and that the division dynamics early in life can predict 127 lifespan