1

2 An aging-independent replicative lifespan in a symmetrically dividing

3

4 Eric C. Spiveya,b,*, Stephen K. Jones Jr.a,b,*, James R. Rybarskia, Fatema A. Saifuddina, and Ilya

5 J. Finkelsteina,b,c,1

6

7 a Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX

8 b Center for Systems and Synthetic , The University of Texas at Austin, Austin, TX

9 c Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX

10 * Equal contribution

11 1 Correspondence: [email protected], Department of Molecular Biosciences, The

12 University of Texas at Austin, Austin, TX 78712. Office: 512-471-1394

13 Abstract

14 The replicative lifespan (RLS) of a —defined as the number of cell divisions before death—

15 has informed our understanding of the mechanisms of cellular aging. However, little is known

16 about aging and longevity in symmetrically dividing eukaryotic cells because most prior studies

17 have used for RLS studies. Here, we describe a multiplexed fission yeast lifespan

18 micro-dissector (multFYLM) and an associated image processing pipeline for performing high-

19 throughput and automated single-cell micro-dissection. Using the multFYLM, we observe

20 continuous replication of hundreds of individual fission yeast cells for over seventy-five

21 generations. Surprisingly, cells die without the classic hallmarks of cellular aging, such as

22 progressive changes in size, doubling time, or sibling health. Genetic perturbations and drugs can

23 extend the RLS via an aging-independent mechanism. Using a quantitative model to analyze

24 these results, we conclude that fission yeast does not age and that cellular aging and replicative

25 lifespan can be uncoupled in a eukaryotic cell.

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27 Introduction

28 Aging is the progressive decrease of an ’s fitness over time. The asymmetric

29 segregation of pro-aging factors (e.g., damaged proteins and ) has been proposed to

30 promote aging in mitotically active yeast and higher (1–4). In budding yeast,

31 asymmetric division into mother and daughter cells ensures that a mother cell produces a limited

32 number of daughters over its replicative lifespan (RLS) (5). Aging mother cells increase in size,

33 divide progressively more slowly, and produce shorter-lived daughters (5–7). Mother cell decline

34 is associated with asymmetric phenotypes such as preferential retention of protein aggregates,

35 dysregulation of vacuole acidity, and genomic instability (3,8,9). By sequestering pro-aging

36 factors in the mothers, newly born daughters reset their RLS (3,8,10). These observations raise

37 the possibility that alternate mechanisms may be active in symmetrically dividing eukaryotic

38 cells.

39 The fission yeast Schizosaccharomyces pombe is an excellent model system for

40 investigating RLS and aging phenotypes in symmetrically dividing eukaryotic cells. Fission

41 yeast cells are cylindrical, grow by linear extension, and divide via medial fission. After cell

42 division, the two sibling cells each inherit one pre-existing cell tip (old-pole). The new tip is

43 formed at the site of septation (new-pole). Immediately after division, new growth is localized at

44 the old-pole end of the cell. Activation of growth at the new-pole cell tip occurs ~30% through

45 the (generally halfway through G2). This transition from monopolar to bipolar growth

46 is known as new end take-off (NETO) (11–13). Prior studies of fission yeast have yielded

47 conflicting results regarding cellular aging. Several papers reported aging phenotypes akin to

48 those observed in budding yeast (e.g., mother cells become larger, divide more slowly, and have

49 less healthy offspring as they age) (2,14). However, a recent report used colony lineage analysis

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50 to conclude that protein aggregates are not asymmetrically distributed, and that inheriting the old

51 cell pole or the old spindle pole body during does not lead to a decline in cell health

52 (15). However, this report tracked the first 7-8 cell divisions of microcolonies on agar plates and

53 thus could not observe the RLS of single cells (15). The controversy between these studies may

54 partially stem from the difficulty in tracking visually identical cells for dozens of generations.

55 Replicative lifespan assays require the separation of cells after every division. This is

56 traditionally done via manual micro-dissection of sibling cells on agar plates; a laborious process

57 that is especially difficult and error-prone for symmetrically dividing fission yeast. Extrinsic

58 effects related to using a solid agar surface may confound observations made under these

59 conditions (16). Finally, recent work using high-throughput microfluidic devices to study

60 individual budding yeast and bacterial cells (17–25) has shown that large sample sizes are

61 needed to truly capture cellular lifespan accurately – populations less than ~100 cells do not

62 reliably estimate the RLS (24).

63 Here, we report the first high-throughput characterization of both RLS and aging in

64 fission yeast. To enable these studies, we describe a microfluidic device—the multiplexed fission

65 yeast lifespan microdissector (multFYLM)—and a software analysis suite that captures and

66 tracks individual S. pombe cells throughout their RLS. Using this platform, we present the first

67 quantitative replicative lifespan study in S. pombe, settling a long-standing controversy regarding

68 whether this organism undergoes replicative aging (2,14,15,26). The RLS of fission yeast is

69 substantially longer than previously reported (2,14). Remarkably, cell death is stochastic and

70 does not exhibit the classic hallmarks of cellular aging. Despite the lack of aging phenotypes,

71 rapamycin and Sir2p overexpression both extend RLS, whereas increased genome instability

72 decreases RLS. These results demonstrate that RLS can be extended without any aging

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73 phenotypes, a fact that has implications for the proposed mechanism of lifespan extension by

74 these interventions. Using these results, we also describe a quantitative framework for analyzing

75 how stochastic and age-dependent effects contribute to the experimentally measured replicative

76 lifespan. This framework will be broadly applicable to future replicative lifespan studies of

77 model . We conclude that fission yeast dies primarily via a stochastic, age-independent

78 mechanism.

79

80 Results

81 A high-throughput microfluidic assay for measuring the replicative lifespan in fission yeast

82 Replicative lifespan assays require the separation of cells after every division. This is

83 traditionally done via manual micro-dissection of sibling cells, a laborious process that is

84 especially difficult for symmetrically dividing fission yeast. We recently developed a

85 microfluidic platform for capturing and immobilizing individual fission yeast cells via their old

86 cell tips (27). However, our first-generation device could only observe a single strain per

87 experiment, required an unconventional fabrication strategy, and suffered from frequent cell loss

88 that ultimately shortened the observation time. To address these limitations, we developed a

89 multiplexed fission yeast lifespan microdissector (multFYLM, Figure 1), along with a dedicated

90 software package designed to streamline the analysis and quantification of raw microscopy data.

91 Single cells are geometrically constrained within catch channels, preserving the orientation of the

92 cell poles over multiple generations (Figure 1A-B). The cells divide by medial fission, thereby

93 ensuring that the oldest cell pole is retained deep within the catch channel. If new-pole tips are

94 loaded initially, then these outward-facing tips become the old-pole tips after the first division.

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95 The multFYLM consists of six closely spaced, independent microfluidic subsystems,

96 with a total capacity of 2,352 cells (Figure 1C, Figure 1 - figure supplement 1). To ensure

97 equal flow rates throughout the device, each of the six subsystems is designed to have the same

98 fluidic resistance. Each subsystem consists of eight parallel rows of 49 cell catch channels

99 (Figure 1D). The catch channel dimensions were optimized for loading and retaining wild type

100 fission yeast cells (Figure 1E, Figure 1 - figure supplement 2; (27). The eight rows of cell

101 catch channels are arranged between a large central trench (40 µm W x 1 mm L x 20 µm H) and

102 a smaller side trench (20 µm W x 1 mm L x 20 µm H). Cells are drawn into the catch channels

103 by flow from the central trench to the side trenches via a small (2 µm W x 5 µm L x ~12 µm H)

104 drain channel (Figure 1F). We did not monitor all 2,352 catch channels during our experiments,

105 but we routinely filled >80% of the 224 monitored catch channels in each of the six microfluidic

106 sub-systems (Figure 1 - figure supplement 2A). A constant flow of fresh media supplies

107 nutrients, removes waste, and ensures that cells are stably retained for the duration of the

108 experiment. Once loaded, up to 98% of the cells could be retained in their respective catch

109 channels for over 100 hours (Figure 1 - figure supplement 2B-C, Movie S1). As a cell grows

110 and divides, the old-pole cell is maintained by flow at the end of the catch channel, while the

111 new-pole cells grow towards the central trench. Eventually, the new-pole cells are pushed into

112 the central trench, where they are washed out by constant media flow (Movie S1). This allows

113 for continuous, whole-lifetime observation of the old-pole cell as well as its newest siblings

114 (Figure 1G-H).

115 An inverted fluorescence microscope with a programmable motorized stage allowed the

116 acquisition of single cell data with high-spatiotemporal resolution (Figure 1 - figure

117 supplement 3A). Following data collection, images are processed through the image analysis

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118 pipeline (see Supplemental Methods), which measures length and fluorescence information for

119 each cell at each time point (Figure 1 - figure supplement 3B). These data are parsed to

120 determine the cell’s size, division times, growth rates, lifespan, and fluorescence information (if

121 any). Cells within the multFYLM grew with kinetics and morphology similar to cells in liquid

122 culture and showed growth and NETO rates (Figure 2 - figure supplement 1, Supplementary

123 file 1A) similar to those reported previously (15,22,28). In sum, the multFYLM allows high

124 content, continuous observation of individual fission yeast cells over their entire lifespans.

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126 The fission yeast replicative lifespan is not affected by aging

127 We used the multFYLM to measure the fission yeast RLS (Figure 2). From these data, we

128 plotted the replicative survival curve (Figure 2A) and determined the survival function, (),

129 which describes the probability of being alive after generation . Using the survival data, we also

() 130 computed the hazard function, () =− ∗ (), which describes the instantaneous risk

131 of death after cell division (Figure 2B). The hazard rate (also called the death rate) can be

132 calculated for any generational age using this function. Surprisingly, the fission yeast survival

133 curve did not fit the traditional aging-dependent Gompertz model, (29–31), which describes

134 survival and hazard in terms of a generation-dependent (i.e., aging) and a generation-independent

135 term (Eq. [2] in Methods). The RLS data was best described by a single exponential decay,

136 corresponding to a generation-independent hazard rate. Strikingly, the hazard rate does not

137 increase as the replicative age increases; instead, it remains steady at an average ~2% chance of

138 death per cell per generation. For comparison, we also analyzed the survival data and hazard

139 function for budding yeast (S. cerevisiae) grown in a microfluidic device (21). As expected, the

140 budding yeast hazard function increases with each generation and fits the aging-dependent

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141 Gompertz model. Thus, the replicative age, , strongly influences the probability of death in

142 budding yeast, but not in fission yeast.

143 The RLS is defined by the number of generations at which 50% of the starting cells are

144 dead. We measured an RLS of 39.2 generations (95% C.I. 38.6-39.8, n=440, Figure 2A and

145 Supplementary Table 2) for the laboratory strain 972h- grown in rich media (32). To explore

146 the effect of different genetic backgrounds on replicative lifespan, we determined survival

147 curves, hazard functions, and replicative half- for three additional strains, including two

148 wild fission yeast isolates with distinct morphologies and doubling times (33). Most laboratory

149 fission yeast strains have three , but the recently identified strain CBS2777 has a

150 fourth that originated via a series of complex genomic rearrangements (34,35,82).

151 CBS2777 had the shortest RLS of 22.7 generations (95% CI 21.9-23.6 generations), consistent

152 with its aberrant genome and altered genome maintenance (34). In contrast, strains NCYC132

153 and JB760 showed greater longevity with estimated RLS of >50 and >70 generations,

154 respectively (Figure 2 - figure supplement 2, Supplementary file 1B). These RLS correspond

155 to a hazard rate that is ~5-fold lower than strain h- 972 (Figure 2 - figure supplement 2B). The

156 longevity of strain NCYC132 is consistent with an earlier manual microdissection study

157 performed on solid media (15). Each strain’s survival curve was best described by an exponential

158 decay function, resulting in a generation-independent hazard function. Thus, an aging-

159 independent replicative lifespan is a feature of diverse fission isolates, and likely the entire

160 .

161

162 Aging-associated phenotypes do not correlate with death in fission yeast

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163 Prior RLS studies have identified three common phenotypes associated with cellular aging

164 (36,37). In budding yeast, aging mother cells increase in cell size, progressively slow their

165 doubling times, and produce daughters with decreased fitness (5–7) . Whether older fission yeast

166 cells also undergo similar aging-associated phenotypes remained unresolved. To answer these

167 outstanding questions, we examined time-dependent changes in morphology, doubling times,

168 and sibling health in individual fission yeast cells as they approached death. Quantitative

169 observation of cell length revealed two classes of dying cells: the majority (72%; n=234) died

170 prior to reaching the normal division length (< 16.2 µm, Figure 3A-B). The remaining cells

171 (28%; n=92) showed an elongated phenotype and exceeded the average division time by more

172 than three-fold. In S. pombe, cell length is strongly correlated with division time and cell length,

173 indicating that cell cycle checkpoints were likely dysregulated in these dying cells (11,12,38,39).

174 Despite these differences in length at death, most cells had normal doubling times throughout

175 their lifespan (Figure 3C-D). Next, we investigated whether there were any morphological

176 changes in the generations preceding death. The majority of cells retained wild type cell lengths

177 and doubling rates until the penultimate cell division. In the two generations immediately

178 preceding death, there was a mild, but statistically significant change in the distributions of cell

179 lengths (Figure 3E) and doubling times (Figure 3F). However, we did not observe any

180 predictable trends for individual cells, arguing against a consistent pattern of age-related decline.

181 In sum, S. pombe dies without any aging-associated morphological changes.

182 We next determined the fate of the last sibling produced by dying cells. This is possible

183 because the last siblings of a dying cell also remain captured in the catch channels (Figure 4A).

184 These are siblings that are produced by the final division of the old-pole cell. We categorized

185 these siblings into three classes: (i) those that died without dividing, (ii) those that divided once

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186 and died, and (iii) those that divided two or more times (Figure 4A, Movies S2-S4). The fate of

187 the new-pole siblings was independent of the replicative age of the old-pole cells at death

188 (Figure 4B). Most new-pole siblings of a dying cell (66%, n=135) never divided and typically

189 did not grow, suggesting that the underlying cause of death was distributed symmetrically

190 between the two sibling cells. Similarly, new-pole siblings that divided once and died (14%,

191 n=29) also typically did not grow. Old pole cells that died while hyper-elongated were more

192 likely to have these unhealthy offspring (Figure 4C-D). As elongated cells generally indicate the

193 activation of a DNA damage checkpoint (40), our findings suggest that genome instability in

194 these cells may be the underlying cause of death in both the old cell and its most recent sibling.

195 In new-pole siblings that divided two or more times (20%, n=40), doubling times were

196 indistinguishable from rapidly growing, healthy cells (2.1 ± 0.7 hours, n=208). These healthy

197 new-pole cells were typically born from old-pole cells that did not elongate during their terminal

198 division (Figure 4C-D). These observations suggest that the cause of death was contained within

199 the old-pole cell, thereby reducing the likelihood of death in the sibling. Taken together, our

200 observations suggest that in 76% of cases, cell death impacts both sibling cells. However, in 24%

201 of cells, death is localized to just one of the two siblings. Importantly, there was no correlation

202 between the replicative age of the old-pole cell and survival probability of the last sibling cell.

203 These observations are in stark contrast to S. cerevisiae, where aging factors are generally

204 sequestered to younger mother cells (3,41). However, older S. cerevisiae mothers produce larger

205 and slower-dividing daughter cells. Thus, we do not observe any aging-dependent outcomes for

206 the fate of the new-pole sibling, further confirming that S. pombe does not age.

207

208 Genetic manipulation and rapamycin treatment extend replicative lifespan

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209 The histone deacetylase Sir2p modulates lifespan and aging in a wide variety of organisms from

210 yeasts to mice (42–44). For example, sir2 deletion (sir2Δ) in budding yeast reduces the

211 replicative lifespan by ~50% (21,45), whereas Sir2p overexpression can increase the RLS by up

212 to 30% (45). The S. pombe genome encodes three Sir2p homologs, one of which shares a high

213 degree of sequence similarity and biochemical functions with the budding yeast Sir2p (46,47).

214 Although S. pombe does not show aging phenotypes, we next investigated whether Sir2p still

215 modulates the RLS. Deletion of Sir2p (sir2Δ) reduced the RLS by 15% (33.4 ± 0.8 generations;

216 n=329) relative to the wild type parental strain (39.2 ± 0.6 generations, n=440; Figure 5A).

217 These results are consistent with prior observations that wild type and sir2Δ cells had similar

218 growth rates when cultured without stressors (2). In contrast, constitutive 2-fold sir2

219 overexpression (sir2OE, Figure 5 - figure supplement 1) increased the RLS over 50%, with a

220 mean replicative lifespan of >60 generations (n=301; Figure 5A). Cells overexpressing Sir2p

221 have a 2-fold lower hazard rate (total risk of death) than wild-type cells (Figure 5B and Figure

222 5 - figure supplement 2). In sum, overexpression of Sir2p increases the RLS of S. pombe, but

223 does not affect aging.

224 The target of rapamycin (TOR) pathway is the central regulator of and aging

225 in eukaryotes (48,49). Rapamycin inhibits TOR proteins in eukaryotes (50,51), reducing cell

226 growth by altering translation initiation and repressing biogenesis. Rapamycin

227 increases the RLS of budding yeast (52), and also increases the longevity of worms, flies and

228 mice (53–55). However, the effect of rapamycin on fission yeast RLS has not been reported.

229 Addition of 100 nM rapamycin to the flow medium increased the RLS by 41% (55.3 generations,

230 95% CI 53.1-57.6, n=184; Figure 5A). As with Sir2p overexpression, treating cells with

231 rapamycin increased their longevity by reducing the aging-independent hazard rate (Figure 5B).

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232 Importantly, both rapamycin and Sir2p overexpression did not affect the lengths or doubling

233 times of cells as they approached death. The cell length at division remained constant for the five

234 generations preceding cell death, whereas the cell doubling time increased only in the last

235 generation before death (Figure 5C-F). These results are consistent with an aging-independent

236 mechanism for RLS extension in S. pombe.

237

238 Ribosomal DNA (rDNA) instability contributes to sudden cell death

239 Ribosomal DNA (rDNA) is a repetitive, recombination-prone region in eukaryotic genomes.

240 Defects in rDNA maintenance arise from illegitimate repair of the rDNA locus and have been

241 proposed to be key drivers of cellular aging (44,56). Fission yeast encodes ~117 rDNA repeats

242 on the third chromosome, and incomplete rDNA segregation can activate the spindle checkpoint

243 (57,58). If unresolved, this can result in chromosome fragmentation and genomic instability (59).

244 Indeed, we observed that 28% of dying cells were abnormally long, suggesting activation of a

245 DNA-damage checkpoint (Figure 3B). Aberrant rDNA structures are processed by the RecQ

246 helicase Rqh1p and in the human Rqh1p homologs are implicated in cancer and

247 premature aging-associated disorders (60–64). Therefore, we sought to determine whether rDNA

248 instability and loss of Rqh1p can contribute to stochastic death.

249 We visualized segregation of rDNA in cells using the nucleolar protein Gar2 fused to

250 mCherry. Gar2 localizes to rDNA during transcription and has been used to monitor rDNA

251 dynamics in live cells (59). The strain expressing Gar2-mCherry at the native locus divided at

252 approximately the same length and rate as wild-type cells (Figure 6 - figure supplement 1).

253 Dividing cells mostly had equal Gar2 fluorescence in both sibling cell nuclei (Figures 6A-B).

254 However, 7% of cells (n=88/1182) showed rDNA segregation defects that appear as multiple

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255 rDNA loci, asymmetric fluorescence distributions, or rDNA bridges (Figure 6A). The rDNA

256 mis-segregation was similar to mis-segregation of a fluorescent marker on chromosome I (7% of

257 cells; n=20/278) and chromosome II (5% of cells; n=19/370) (Figure 6 - figure supplement 2).

258 Multi-punctate rDNA loci were nearly always lethal, whereas asymmetric rDNA segregation and

259 rDNA bridges were lethal in 40% of cells (n=31/78; Figure 6B). Importantly, 40% of cells

260 (n=56/141) showed rDNA defects immediately preceding death. In dying cells, rDNA instability

261 was elevated relative to mis-segregation of chromosome I (16% of cells; n=15/95) and

262 chromosome II (13% of cells; n=18/137) (Figure 6 - figure supplement 2). Loss of Rqh1p

263 caused higher frequencies of spontaneous rDNA defects, a shorter RLS (5.3 generations, 95% CI

264 5.0-5.7), and a > 7-fold higher hazard rate (Figure 6 - figure supplement 3). rDNA defects were

265 even more prevalent in dying rqh1Δ cells (Figure 6 - figure supplement 3). We next tested the

266 influence of Sir2p over-expression or addition of rapamycin on the short RLS of rqh1Δ cells.

267 Addition of rapamycin showed a mild RLS extension, whereas the rqh1Δ sir2OE strain was

268 extremely short lived (Figure 6 – figure supplement 3). These data suggest that the elevated

269 rDNA instability cannot be rescued by Sir2p and is only partially rescued by rapamycin. In

270 summary, the longevity of fission yeast is promoted by rDNA stability.

271

272 Discussion

273 Here, we report the first study of fission yeast RLS in a high-throughput microfluidic

274 device (Figure 1). The multFYLM provides a temperature-controlled growth environment for

275 hundreds of individual cells for up to a week (>75 generations), facilitating tens of thousands of

276 micro-dissections. We also developed an image-processing pipeline for quantitative phenotypic

277 analysis of individual cell lineages. The blueprints for multFYLM, as well as the image analysis

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278 pipeline are both available via GitHub (see Methods). We note that microfluidics-based lifespan

279 measurements are free from potential extrinsic effects (e.g., secretion of small molecules onto the

280 solid agar surface) which have been proposed to confound observations of RLS assays using

281 manual microdissection (16). Using the multFYLM, we set out to determine whether fission

282 yeast undergoes replicative aging.

283 Taken together, our data provide three sets of experimental evidence that fission yeast

284 does not age. First, the RLS survival curves and corresponding hazard rates are qualitatively

285 different between budding and fission yeasts (Figure 2). The budding yeast RLS is best

286 described by a Gompertz model that includes both an age-dependent as well as an age-

287 independent survival probability. In contrast, the fission yeast RLS is best described by a single

288 exponential decay. This corresponds to a hazard function ℎ() that is constant with each

289 generation, as would be expected for an organism that does not age. Second, we observed that

290 cell volumes and division rates did not change until the penultimate cell division (Figure 3).

291 Third, after the death of a cell, the health of the surviving sibling was also independent of age

292 (Figure 4). These conclusions are also broadly consistent with a recent report that observed

293 robust, aging-independent growth of the symmetrically dividing E. coli in a microfluidic device

294 (19).

295 Our results highlight a critical consideration when interpreting replicative lifespan curves.

296 The experimentally determined RLS does not necessarily report on whether a population of cells

297 is aging. This is because the RLS is affected by a combination of both age-independent and age-

298 dependent biological processes—cell may die randomly without aging. A mathematical model is

299 required to further parse the relative contributions of aging-independent and aging-dependent

300 mechanisms. The Gompertz function is an excellent model for understanding the replicative

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301 lifespan survival curve (65). In this model, the replicative lifespan is dependent on just two

302 parameters: (i) an age-independent hazard rate, and (ii) an age-dependent hazard rate, which

303 determines how rapidly mortality increases as a function of the cell’s age (66). Together, these

304 two parameters describe a population’s likelihood of death at any age. Figure 7 illustrates that the

305 experimentally observed RLS is dependent on both of these terms. An increased RLS is not

306 sufficient to conclude that cells age more slowly. Indeed, our data indicate that a decreased RLS

307 can also be achieved by increasing the age-independent hazard rate. To further illustrate this

308 point, we include a Gompertz model analysis of the RLS of S. cerevisiae grown in a microfluidic

309 device (21) and our own data from S. pombe. For all of our data, changes in RLS can be

310 completely accounted for by changes in the age-independent hazard rate. In contrast, budding

311 yeast have age-dependent hazard rates an order of magnitude higher than their age-independent

312 rates (Figure 7). We anticipate that the development of the multFYLM, as well as a quantitative

313 analysis framework, will continue to inform comparative RLS studies in model organisms.

314 What are the molecular mechanisms of stochastic cell death in a symmetrically dividing

315 eukaryote? Strikingly, the RLS of S. pombe can be extended by up to 41% via rapamycin

316 treatment and Sir2p over-expression without influencing aging-related phenotypes (Figure 5).

317 This suggests that rapamycin and Sir2p both reduce the onset of sudden death. To our

318 knowledge, this is the first evidence these interventions can extend the RLS in an organism that

319 does not age. This suggests that both Sir2p and rapamycin may partially affect the RLS of aging

320 organisms like S. cerevisiae via an aging-independent mechanism. Additional studies will be

321 required to parse out the aging-dependent and aging-independent mechanisms of RLS extension.

322 For a subset of cells, genomic instability due to aberrant segregation of the rDNA locus

323 may be one cause of death (9,44). Consistent with this hypothesis, rDNA segregation defects

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324 were highly elevated relative to general chromosome mis-segregation in wild type cells

325 immediately prior to death (Figure 6). Ablating Rqh1p, a helicase that promotes rDNA stability

326 and replication fork progression (67–69), further increased rDNA defects while drastically

327 reducing the RLS, independent of age. Recent studies also indicate that protein aggregation may

328 be a second major cause of sudden death in rapidly dividing fission yeast cells (15,70). These

329 two mechanisms of death need not be mutually exclusive. Future studies will further define the

330 molecular mechanisms of aging-independent death in fission yeast.

331

332 MATERIALS AND METHODS

333 multFYLM master structure photolithography

334 Master structures were fabricated using SU-8 3005 photoresist for the first layer and SU-8 2010

335 photoresist for the second layer, following standard photolithography techniques described in the

336 product literature (Microchem, Westborough, MA). Photoresist was spun onto 100 mm-diameter,

337 test-grade, P-doped silicon wafers (ID# 452, University Wafers). Photoresist thickness was 20-

338 30 µm for the first layer (conduits, central and side trenches), and 5-6 µm for the second layer

339 (catch and drain channels). Custom chrome on quartz photomasks (Compugraphics) were

340 manufactured from designs created using the freeware integrated circuit layout editor Glade

341 (www.peardrop.co.uk) or with OpenSCAD (www.openscad.org). A Suss MA-6 Mask Aligner

342 (Suss MicroTec Lithography GmbH) was used for mask alignment and photoresist exposure.

343

344 multFYLM construction

345 Approximately 25 g of polydimethylsiloxane (PDMS, Sylgard 184, Dow Corning) was mixed at

346 a weight ratio of 10:1 polymer:hardener, placed on a rotator for >30 minutes, then centrifuged to

347 remove bubbles. A tape barrier was applied to the edge of a silicon wafer bearing the multmult

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348 master structures, and 13 g of PDMS was poured onto the wafer, covering the surface. The wafer

349 with PDMS was degassed for ~10 minutes (at 60-70 cmHg vacuum in a vacuum chamber) to

350 remove additional bubbles, then placed in a 70°C oven for 15-17 minutes. The wafer was

351 intentionally removed while the PDMS was still tacky to improve adhesion of the microfluidic

352 connectors (nanoports). To create a source and drain interface for the device, nanoports (N-333-

353 01, IDEX Health and Science, Oak Harbor, WA) were applied to the surface of the PDMS over

354 the ends of the master structure of the multFYLM, then an additional 14 g of PDMS was poured

355 over the surface, with care taken to avoid getting liquid PDMS inside the nanoports. The wafer

356 with PDMS and nanoports was then degassed >10 minutes to remove additional bubbles, and

357 then returned to the 70°C oven for 3 hours to cure completely. The cured PDMS was then

358 removed whole from the wafer after cooling to room temperature. A multFYLM with nanoports

359 was trimmed from the cured PDMS disk to approximately 15 x 25 mm. A 1 mm diameter biopsy

360 punch (Acu-punch, Accuderm) was used to make holes connecting the bottom of each nanoport

361 to the conduits on the bottom surface of the PDMS. The multFYLM was then placed in

362 isopropanol and sonicated for 30 minutes, removed, and placed to dry on a 70°C hotplate for 2

363 hours. Borosilicate glass coverslips (48 x 65 mm #1, Gold Seal) were concurrently prepared by

364 cleaning for >1 hour in a 2% detergent solution (Hellmannex II, Hellma Analytics), then rinsing

365 thoroughly in deionized water and isopropanol before drying for >2 hours on a 70°C hotplate.

366 Cleaned multFYLM and coverslips were stored in a covered container until needed. To bond, the

367 multFYLM and a coverslip were placed in a plasma cleaner (PDC-32G, Harrick Scientific) and

368 cleaned for 20 seconds on the “high” setting with oxygen plasma from ambient air (~21%

369 oxygen). The multFYLM and glass coverslip were then gently pressed together to form a

370 permanent bond. To make the microfluidic interface, PFA tubing (1512L, IDEX), with a 1/16”

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371 outer diameter was used to connect a 100 mL luer lock syringe (60271, Veterinary Concepts,

372 www.veterinaryconcepts.com) to the multFYLM, and to construct a drain line leading from the

373 multFYLM to a waste container. The tubing was connected to the multFYLM nanoports using

374 10-32 coned nuts (F-332-01, IDEX) with ferrules (F-142N, IDEX). A two-way valve (P-512,

375 IDEX) was placed on the drain line to allow for better flow control. For longer experiments, two

376 syringes could be loaded in tandem, connected with a Y-connector (P-512, IDEX). The valve

377 and Y-connector were connected to the tubing using ¼-28 connectors (P-235, IDEX) with

378 flangeless ferrules (P-200, IDEX). The source line tubing was connected to the syringe with a

379 Luer adapter (P-658, IDEX). The syringe(s) was/were then placed in a syringe pump (Legato

380 210, KD Scientific) for operation. A 2 µm inline filter (P-272, IDEX) was placed upstream of the

381 multFYLM to reduce the chance of debris clogging the multFYLM during operation.

382

383 Loading the multFYLM with cells

384 As soon as possible after plasma bonding (usually within 15 minutes), the multFYLM was

385 placed on the microscope stage and secured, then 15 µL of cells suspended in YES media with

386 2% BSA were gently injected into the source nanoport. The drain and source interface tubing

387 (with drain valve closed) were then connected to the drain and source nanoports. The syringe

388 pump was then started at 40 µL min-1, and the drain valve was opened to allow flow to start. The

389 syringe pump was adjusted between 20-60 µL min-1 until flow was established, and catch

390 channels were observed to be filling with cells, then a programmed flow cycle was established:

˗ 391 1-5 minutes at 50 µL min 1 followed by 10-14 minutes at 5 µL min-1 (average flow rate, 0.5-1.2

392 mL h-1).

393

18

394 Time-lapse imaging

395 Images were acquired using an inverted microscope (Nikon Eclipse Ti) running NIS Elements

396 and equipped with a 60X, 0.95 NA objective (CFI Plan Apo λ, Nikon) and a programmable,

397 motorized stage (Proscan III, Prior). The microscope was equipped with Nikon’s “Perfect Focus

398 System” (PFS), which uses a feedback loop to allow consistent focus control over the multiple-

399 day experiments. Images were acquired approximately every two minutes using a scientific-

400 grade CMOS camera (Zyla 5.5, Andor). To improve contrast during image analysis, images were

401 acquired both in the plane of focus and 3-4 µm below the plane of focus. Temperature control

402 was maintained using an objective heater (Bioptechs) and a custom-built stage heater (Omega)

403 calibrated to maintain the multFYLM at 30-31°C.

404 Fluorescence images were acquired with a white light LED excitation source (Sola II,

405 Lumencorp) and a red filter set (49004, Chroma). Fluorescent images were acquired concurrently

406 with white light images, but only every four minutes.

407

408 Media and Strains

409 All strains were propagated in YES liquid media (Sunrise Scientific) or YES agar (2%) plates,

410 except where otherwise noted. A complete list of strains is reported in Supplemental Table 3.

411 Deletion of sir2 in the wild-type 972h- strain was completed by first generating a PCR product

412 containing KANMX4 flanked by SIR2 5’ and 3’ untranslated regions. Competent IF30 cells were

413 then transformed with the PCR product and selected on YES+G418 agar plates. Deletion was

414 confirmed by PCR. A SIR2 over-expression was generated via Gateway cloning (

415 Technologies). First, SIR2 was integrated into the pDONR221 plasmid to yield pIF133. SIR2 was

416 then swapped from pIF133 to pDUAL-GFH1 (Riken), yielding plasmid pIF200. pIF200

19

417 expresses SIR2 under the NMT1 promoter, and also labels the protein C-terminally with a eGFP-

418 FLAG-His6 tags. Plasmid pIF200 was then transformed into strain IF140 for integration at the

419 leu1 locus to generate clones IF230 and IF231(71). Integration was checked by PCR and

420 expression was determined by RT-qPCR. Wild-type 972h- and Gar2-mCherry fusion protein

421 expressing strains (Megan King) were transformed with a PCR product containing HPHMX6

422 (hygromycin resistance) flanked by rqh1 5’ and 3’ untranslated regions. Transformants were

423 selected on YES+hygromycin agar plates, then confirmed by PCR. Chromsome I and II reporter

424 strains were kindly provided by Christian Haering (strains CH2774 and CH3245) (72).

425

426

427 Western Blotting

428 Strains IF235 (sir2Δ), IF140 and IF230 (sir2 over-expression; sir2OE) were grown in synthetic

429 complete media (Sunrise Scientific) to OD 1.0, and whole cell extracts were prepared by

430 trichloroacetic acid precipitation with bead lysis (73). These cell extracts were combined with

431 loading buffer, boiled for 5 minutes at 95°C, and resolved via a 4-20% TGX SDS-PAGE gel

432 (Bio-Rad). Proteins were transferred to a PVDF membrane, and then blocked with PBS Odyssey

433 blocking buffer (Li-Cor, Inc.) for one hour with shaking at 22°C. The membrane was incubated

434 overnight at 4°C while shaking in phosphate buffered saline + 0.05% Tween-20 (PBST)

435 containing 1:1000 dilution of anti-β actin antibody (mouse, ab8224, Abcam) and 1:500 dilution

436 of an S. pombe-specific anti-Sir2p antibody (rabbit, generously provided by Dr. Allshire) (74).

437 The membrane was washed once in PBST, and then agitated for 4 hours at 22˚C with 1:10,000

438 dilution of goat anti-mouse IgG and 1:10,000 dilution of goat anti-rabbit IgG (IRDye 680RD and

20

439 IRDye 800CW, respectively, Li-Cor, Inc.). The membrane was washed three times with PBST

440 and imaged on an Odyssey CLx dual-channel imaging system (Li-Cor, Inc.).

441

442 FYLM Critic: An open-source image processing and quantification package

443 To analyze our single-cell data, we developed FYLM Critic—a Python package for rapid and

444 high-content quantification of the time-lapse microscopy data (github.com/finkelsteinlab/fylm).

445 The FYLM Critic pipeline consists of two discrete stages: (i) pre-processing the raw microscope

446 images to correct for stage drift, and (ii) quantification of cell phenotypes (e.g., length, doubling

447 time, fluorescence intensity and spatial distribution).

448 Stage 1: Pre-processing the microscope images

449 Several image pre-processing steps were taken to permit efficient quantification.

450 First, the angles of the four solid sections of PDMS on either side of the catch channels were

451 measured relative to the vertical axis of the image, and a corrective rotation in the opposite

452 direction was applied to all images in that field of view. This resulted in the long axis of each cell

453 being completely horizontal in all subsequent analyses. The multFYLM was too large to be

454 imaged in its entirety within a single field of view, so the microscope stage was programmed to

455 move to eight different subsections of the device in a continuous cycle. Due to the imperfect

456 motion of the microscope stage, subsequent images of the same field of view randomly

457 translated by a few micrometers relative to the previous image at the same nominal location.

458 Images were aligned to the first image of the sequence using a cross-correlation algorithm (75).

459 The exact location of each catch channel was manually specified, but only if a cell had already

460 entered the channel at the beginning of the experiment.

461 Stage 2: Annotating individual cells within the multFYLM

21

462 In order to analyze cell lengths and phenotypes, kymographs were produced for each catch

463 channel and then annotated semi-manually. To produce the kymographs, a line of pixels was

464 taken along the center of each catch channel, starting from the center of the “notch” and

465 extending into the central trench. This kymograph captured information along the long axis of

466 the cells. The time-dependent one-dimensional (1D) kymographs for each catch channel were

467 stacked together vertically, producing a two-dimensional (2D) kymograph of each cell’s growth

468 and division. The out-of-focus image was used for this process as the septa and cell walls were

469 much more distinct, as described previously (22). The position of the old pole and the leftmost

470 septum were then manually annotated using a simple point-and-click interface to identify each

471 cell division. Once all single-cell kymographs were annotated, this annotation was used to

472 calculate a cell length for each time point. The final status of the cell (whether it died, survived to

473 the end of the experiment, or was ejected) was determined by the user. Further analysis of the

474 cell length, growth rates, and fluorescence intensities was performed in MATLAB (Mathworks).

475 The local minima of the growth curves were used to determine division times and the replicative

476 age of each cell.

477 Ejections and replacement of individual cells within the catch channels may confound our

478 whole-lifespan tracking. Therefore, we optimizing cell loading, retention and imaging conditions

479 to minimize the loss of individual cells throughout the course of each experiment. First, ejection

480 of individual cells was minimized over the course of ~100 hours (Figure 1 - figure supplement

481 2B). Second, each cell was imaged once every two minutes, minimizing the amount of time

482 during which a cell could conceivably be ejected and replaced by another from the central trench.

483 If a cell were indeed replaced within our two-minute frame rate, we would expect an abrupt

484 change in the captured cell length (indicated a re-loading event). All kymographs were carefully

22

485 monitored for such events, and in the rare cases where this occurred, were discarded from the

486 final analysis. In summary, our analysis ensures that cell ejection or re-capture is mitigated by

487 both efficient cell capture and by analysis of the single-cell datasets.

488

489 Survival curves

490 Kaplan-Meier (76) survival function estimates were calculated in MATLAB. Due to the low

491 number of lost cells (Figure 1 - figure supplement 2B) and the high total number of total cells

492 (n ≥100 for all experiments), the survival curves did not include cells that were lost during the

493 course of the experiment (i.e., there was no right censoring). Our analysis is consistent with

494 similar studies in S. cerevisiae (18). The survival function estimates were fit with either a simple

495 exponential decay function

() =∗ [1]

496 or the Gompertz survival function (29,30)

() =/( ) [2]

497 Where S is the fraction of the initial population surviving at generation . The coefficients

498 & are >0 and <1, and are further defined for the hazard function () below. The plotted

499 survival data were weighted at 1/() to increase the influence of older cells on the fit.

500 The hazard function () (the probability of cell death during a given generation) can be derived

501 from the survival function:

[3] −() () = ()

502 where is the first derivative of . For a survival curve fit to an exponential decay function, the

503 corresponding hazard function is a constant:

23

() = [4]

504 For a survival curve fit to the Gompertz function, the hazard function simplifies to:

() =∙ [5]

505 Where the coefficient amplitude-scales (), and so has an age-independent influence on the

506 hazard function, while the coefficient time-scales (), and so provides a compact method to

507 describe the age-dependent increase in the probability of cell death (Figure 7). Note that as

508 →0, Equation [5] approaches Equation [4].

509 Mean replicative lifespan (RLS) can be calculated by setting () = ½, and solving for (77).

510 For exponential decay, RLS is simply the half-life of the decay function:

= (2)/ [6]

511 For the Gompertz function, RLS is:

[7] ln(2) ln 1+ =

512

513 Statistical Data Analysis

514 All fitting and statistical data analysis was performed in MATLAB. Briefly, datasets were first

515 tested for normality using the Anderson-Darling method, and parametric tests were used when

516 possible. Fitting of all data was performed in MATLAB using either the nonlinear least squares

517 method or least squares method. Determination of whether a survival curve was fit to an

518 exponential decay or Gompertz function was made primarily by selecting the fit with the highest

519 adjusted r2 value.

520

521

24

522 The authors declare no conflict of interest.

523 This article contains supporting information online.

524

525 Acknowledgments

526 We are indebted to our colleagues Jürg Bähler, Christian Haering, Megan King, Edward

527 Marcotte, Akihisa Matsuyama, Ronit Weisman, and Blerta Xhemalce for valuable strains and

528 reagents. We are grateful to Xenia Brianna Gonzalez for her assistance with data quantification,

529 and to other members of the Finkelstein laboratory for carefully reading the manuscript. We

530 thank our colleagues Edward Marcotte, Andreas Matouschek and Aashiq Kachroo for critical

531 feedback. This work was supported by the American Federation of Aging Research (AFAR-

532 020), the National Institute of Aging (F32 AG053051 to S.K.J.), CPRIT (R1214 to I.J.F.), and

533 the Welch Foundation (F-l808 to I.J.F.). I.J.F. is a CPRIT Scholar in Cancer Research. The

534 content is solely the responsibility of the authors and does not necessarily represent the official

535 views of the National Institutes of Health.

536

25

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771 FIGURES

772 Figure 1. A multiplexed fission yeast lifespan microdissector (multFYLM). (A) Illustration 773 of multFYLM (gray) loaded with fission yeast (orange). Blue arrows represent media flow 774 though the multFYLM. (B) Left: Fission yeast cells initially grow from the old pole end 775 (magenta). After new end takeoff (NETO), growth begins at the new pole end (green). Right: 776 multFYLM permits tracking of the old pole cell, as well as its most recent siblings. (C) Optical 777 image of a multFYLM showing six independent channels. Arrows indicate direction of media 778 flow. Scanning electron micrographs of (D) a multFYLM section and (E) a single catch channel. 779 The channel is long enough to accommodate the old pole cell, as well as the most recent new 780 pole sibling. (F) White-light microscope image of a row of catch channels loaded with cells. (G) 781 Time-lapse images (H) and single-cell traces of a replicating cell. The old pole (magenta) is held 782 in place while the new pole (green) is free to grow. The old pole of the most recent sibling 783 (black) extends until it is removed by flow into the central trench (after ~2 h 30 m).

784 Figure 1 - figure supplement 1. Schematic of the multiplexed fission yeast lifespan 785 microdissector (multFYLM). (A) The parallel subunits are designed to have the same fluidic 786 resistance. This ensures similar flow rates through all devices when equal pressure is applied. (B) 787 Detail showing the arrangement of central and side trenches of a single subunit. (C) Detail 788 showing arrangement of the catch channels relative to the central and side trenches. (D) Detail 789 showing the dimensions of one catch channel.

790 Figure 1 - figure supplement 2. Loading and retention of cells in the multFYLM. (A) 791 Loading efficiency of each subunit in the multFYLM. Error bars: S.D. of the mean of three 792 loading experiments. (B) Retention efficiency of cells in the multFYLM under constant media 793 flow. Three representative experiments shown, with the associated number of loaded cells. We 794 observed retention efficiencies as high as 99% over 140 hours. (C) Image of a single field of 795 view of one subunit. White triangles mark captured cells. Scale bar: 25 µm. 796 797 Figure 1 - figure supplement 3. Experimental apparatus and image processing workflow. 798 (A) Schematic of experimental apparatus. Blue lines represent flow of media, red lines represent 799 temperature control, and black lines represent computer-controlled mechanical processes. A 800 syringe pump delivers a constant flow of fresh media to the multFYLM. (B) FYLM Critic 801 software workflow. Image stacks are first registered to remove jitter associated with moving the 802 microscope stage. The user then selects cells of interest with a graphical user interface (GUI), 803 and the software generates kymographs. Kymographs are annotated via a semi-automated GUI 804 with the user helping to trace the growth of each cell. The software then quantifies the trace data 805 and exports associated white-light and fluorescence intensities for downstream processing in 806 MATLAB.

807 Figure 2. The fission yeast replicative lifespan (RLS). (A) Survival curves for wild-type S. 808 pombe (black) and wild-type S. cerevisiae (brown, data from Jo et al. 2015); both were grown in 809 microfluidic microdissection devices. Numbers indicate the average lifespan. Red lines are a fit 810 to a Gompertz (S. cerevisiae) and exponential decay (S. pombe) survival models. Shading 811 indicates 95% confidence interval (C.I.). Dashed blue line: 50% survival. (B) Hazard rate curves

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812 for the data shown in (A). The hazard rate increases dramatically with increased replicative age 813 for S. cerevisiae but not for S. pombe.

814 Figure 2 - figure supplement 1. Health of cells in the multFYLM. (A) Histogram of the 815 doubling times and (B) division lengths of wild-type S. pombe (h- 972) observed in the 816 multFYLM. The doubling time was 2.05 ± 0.45 hours (mean ± S.D., n = 13,453 cells). The mean 817 length at division was 16 ± 2.2 µm. (C) Four representative examples of new end take-off 818 (NETO). Each graph shows the normalized increase in the length of a randomly selected cell 819 over a normalized time comprising one generation. (D) Doubling time did not generally change 820 with age. Horizontal error bars are the SD of the mean time at division for a given generation. 821 Number of cells used ranged from n=517 at generation 1, to n=147 at generation 53. The number 822 of cells declines due mostly to cell death during the experiment.

823 Figure 2 - figure supplement 2. Survival and hazard curves for wild type fission yeast 824 isolates. (A) Survival curves of several wild-type strains. The replicative half-life is indicated in 825 parenthesis. Red lines: exponential decay fit to the data. For the two particularly long-lived 826 strains NCYC132 and JB760, we could only estimate a lower bound on the mean replicative 827 lifespan. (B) Hazard functions of strains shown in (A). Inset: hazard rates normalized to the wild- 828 type (h- 972) hazard rate (error bars indicate 95% CI). The constant hazard rates illustrate that 829 none of the WT populations observed are aging.

830 Figure 3. Fission yeast does not show signs of aging. (A) Images of cells showing a short (top) 831 and long (bottom) phenotype at death. Triangles indicate the old-pole, new-pole, and the new- 832 pole of the previous division as in Figure 1G. Scale bar: 10 µm. (B) Histogram of cell length at 833 death. The birth length was 8.3 ± 1.5 µm (mean ± st. dev., n=326) and the length at division was 834 16 ± 2.2 µm (n=326). (C) Cells dying with either short (top) or long (bottom) phenotype have 835 normal length and (D) doubling times prior to death, as indicated by five representative cells. (E) 836 Distribution of length and (F) doubling time at division in the five generations preceding death. 837 Cells were post-synchronized to the time of death. The black bar shows the median value for 838 each generation (n > 290 cells for all conditions). Sequential generations were compared using 839 the Kolmogorov-Smirnov test (* for p<0.05, ** for p<0.01).

840 Figure 4. Analysis of siblings born during the last division of a dying cell. (A) Last new-pole 841 sibling continued to divide (top), died after one division (middle) or died without dividing 842 (bottom). (B) The distribution of last-sibling phenotypes as a function of the old pole replicative 843 age (n=245). Error bars are st. dev. measured by bootstrap analysis. (C) The length at division 844 and (D) the doubling time of the new-pole siblings. Vertical black lines and gray bars show the 845 mean and standard deviation of the total cell population.

846 Figure 5. Sir2p and rapamycin extend replicative lifespan. (A) Replicative lifespans and (B) 847 hazard rates of strains normalized to the mean RLS and hazard rate of wild-type (h- 972) strain. 848 Error bars: 95% C.I. on an exponential decay fit to the experimental survival curve. (C) 849 Distribution of normalized doubling time in the five generations preceding death for Sir2p 850 overexpression cells and (D) wild-type cells treated with 100 nM rapamycin. (E) Distribution of 851 normalized length at division in the five generations preceding death for Sir2p overexpression

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852 cells and (F) wild-type cells treated with 100 nM rapamycin. Black bars show the median value. 853 Sequential generations were compared using the Kolmogorov-Smirnov test (* for p<0.05, ** for 854 p<0.01; n>144 cells for all conditions).

855 Figure 5 - figure supplement 1. Sir2p expression levels. (A) sir2 mRNA transcript levels as 856 measured by qPCR. The sir2Δ strain was included as a negative control. Error bars: normalized 857 SEM of at least 3 replicates. (B) Sir2p protein levels as detected by an anti-Sir2p antibody. Actin 858 is included as loading control. Sir2p-eGFP migrates slower than endogenous Sir2p. (C) Single 859 cell fluorescence imaging confirms that Sir2p-eGFP is expressed and localizes to the nucleus. 860 Scale bar: 20 µm.

861 Supplemental Figure 5 - figure supplement 2. The effect of Sir2p and rapamycin on the 862 RLS of fission yeast. (A) The replicative half-life is indicated in parenthesis. A lower bound is 863 reported for the long-lived Sir2p overexpression strain. Red lines represent an exponential decay 864 fit to the curve. (B) Hazard functions of strains shown in (A). The constant hazard rates illustrate 865 that none of the WT strains are aging.

866 Figure 6. Ribosomal DNA (rDNA) instability is highly correlated with cell death. (A) 867 Images of cells exhibiting rDNA instability, as reported by gar2-mCherry, which binds to rDNA. 868 (B) Likelihood of cell death following one of the defects observed in (A). (C) Dying cells 869 exhibited elevated rDNA defects (outer ring) relative to healthy dividing cells (inner ring).

870 Figure 6 - figure supplement 1. Strains expressing gar2-mCherry maintain wild-type 871 replication rates. (A) Histogram of doubling times of a strain expressing gar2-mCherry. The 872 doubling time is 2.83 ± 0.87 hours (mean ± st. dev.; n = 1547). (B) Histogram of length at 873 division. The length is 18.0 ± 2.3 µm (mean ± st. dev.; n = 1547).

874 Figure 6 - figure supplement 2. Live imaging of chromosome mis-segregation rates. (A) 875 Chromosome loci were labeled with a 112-tetO repeat. TetR-tdTomato binds this array and 876 allows live cells imaging of chromosome dynamics. Schematic adapted from (72). (B) Images of 877 cells with chromosome I and chromosome II labeled with the tetO-TetR array. (C) Chromosome 878 mis-segregation defects were characterized as in figure 6. Norm: equal separation of fluorescent 879 foci between siblings, Multi: multiple foci observed in single sibling, Uneq: unequal 880 fluorescence between siblings, Bridge: foci remain connected post-division. Dying cells showed 881 2.3-fold elevated chromosome I and (D) 2.6-fold elevated chromosome II mis-segregation 882 defects (outer ring) relative to healthy dividing cells (inner ring).

883 Figure 6 - figure supplement 3. Characterization of the RLS of an rqh1Δ strain. (A) 884 Survival curve of WT (black, 95% CI: 38.6-39.8 generations), rqh1Δ (green, 95% CI: 5.0-5.7), 885 rqh1Δ + 100 nM rapamycin (blue, 95% CI: 6.2-7.2), and rqh1Δ sir2OE (orange). The extremely 886 short lifetime of the rqh1Δ sir2OE strain precluded an accurate fit. (B) Hazard curves for the 887 strains shown in (A). The number of cells, RLS, hazard rate, and goodness of fit parameters are 888 summarized in Supplemental Table 2. (C) The incidence of nucleolar defects for all divisions 889 (center ring) and preceding death (outer ring) in rqh1Δ strain.

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890 Figure 7. The replicative lifespan is an incomplete reporter of cellular aging. RLS contours 891 were generated from experimentally determined ranges of Gompertz coefficients using Equation 892 [7]. Fission yeast strains examined in this study and budding yeast from an analogous study (21) 893 were plotted on the chart based on the coefficient values from either a Gompertz or exponential 894 decay fit. In all cases, the 95% CI of the coefficients was smaller than the marker size.

895 Supplemental Movie 1. Operation of the multFYLM. Time-lapse imaging of fission yeast in a 896 single field of view of the multFYLM for approximately 140 hours. Scale bar is 20 µm.

897 Supplemental Movie 2. New-pole sibling phenotypes. Time-lapse imags of a cell where the 898 last division produces a healthy new-pole cell that divides more than once. Scale bar is 20 µm.

899 Supplemental Movie 3. New-pole sibling phenotypes. Time-lapse images of a cell where the 900 last division produces an unhealthy new-pole cell that divides once before dying. Scale bar is 20 901 µm.

902 Supplemental Movie 4. New-pole sibling phenotypes. Time-lapse images of a cell where the 903 last new-pole cell does not divide. Scale bar is 20 µm.

904 Supplemental File. Supplemental Tables 1-3. This supplemental file contains tables of data 905 used in making figures and also a table of strains used. It references 4 works not referenced in 906 the main text: Wood and Nurse, 2013 (78); Xhemalce and Kouzarides, 2010 (79); Weisman et 907 al., 2005 (80); and Nakazawa et al., 2008 (81)

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