STEM CELLS AND REGENERATION RESEARCH ARTICLE

Aging induces aberrant state transition kinetics in murine musclestem cellsJacob C. Kimmel1,2,3,*, Ara B. Hwang1, Annarita Scaramozza1, Wallace F. Marshall2,3,‡ and Andrew S. Brack1,‡

ABSTRACTMurine muscle stem cells (MuSCs) experience a transition fromquiescence to activation that is required for regeneration, but itremains unknown if the trajectory and dynamics of activation changewith age. Here, we use time-lapse imaging and single cell RNA-seq tomeasure activation trajectories and rates in young and aged MuSCs.We find that the activation trajectory is conserved in aged cells, andwe develop effective machine-learning classifiers for cell age. Usingcell-behavior analysis and RNA velocity, we find that activationkinetics are delayed in agedMuSCs, suggesting that changes in stemcell dynamics may contribute to impaired stem cell function with age.Intriguingly, we also find that stem cell activation appears to be arandom walk-like process, with frequent reversals, rather than acontinuous linear progression. These results support a view of theaged stem cell phenotype as a combination of differences in thelocation of stable cell states and differences in transition ratesbetween them.

KEY WORDS: Aging, Muscle stem cell, Single cell RNA-seq,Time-lapse imaging, State transition, Stem cell activation

INTRODUCTIONStem cells play a keystone role in tissue homeostasis andregeneration in mammalian tissues. During homeostasis, stemcells in multiple systems maintain a noncycling quiescent state(Fuchs, 2009). In the event of injury, quiescent stem cells undergo adynamic process of activation, generating biomass, restructuringcellular geometry, altering metabolism (Ryall et al., 2015), andentering the cell cycle to produce progenitor daughters (Brack andRando, 2012). Impaired stem cell activation has been shown toimpair regenerative potential in multiple tissues (Megeney et al.,1996; Zou et al., 2018). Likewise, ‘priming’ of activation bysystemic signaling factors has been reported to improve regeneration(Rodgers et al., 2014, 2017).

As murine-muscle stem cells (MuSCs) age, the proportion ofcells in regenerative states declines, and the overall regenerativecapacity of the stem cell pool is greatly diminished (Blau et al.,2015; Brack and Muñoz-Cánoves, 2016). Age-related decline inregenerative potential has been attributed to differences in cellsignaling (Bernet et al., 2014; Brack et al., 2007; Conboy et al.,2003; Cosgrove et al., 2014) and proliferative history (Chakkalakalet al., 2012). These differences in regenerative potential betweenstem cells are traditionally viewed as the result of differences in thecharacteristics of stable cell phenotypes (Altschuler and Wu, 2010;Blau et al., 2015; Chakkalakal et al., 2012).

However, aged MuSCs have been reported to show impairedactivation in multiple studies, suggesting that a defect in activationdynamics may also contribute to impaired regeneration (Brack et al.,2007; Cosgrove et al., 2014; Egerman et al., 2015; Gilbert et al.,2010). Differences in activation with age could be the result of cellstaking different paths through state space, or the result of cellsmoving along the same path at different rates. Impaired activationwith age may therefore be explained by one of two models, or acombination of the two.

In the first model (different paths), the location of cell states isshifted by age, such that aged cells at a particular point in theactivation process exhibit different phenotypes than those of youngcells at the same point in the process. In the second model (differentrates), aged and young cells traverse a similar phenotypic pathduring activation, but take different amounts of time to reach a givenpoint in the process. In this model, differences in young and agedphenotypes are primarily the result of changes in activationdynamics (Fig. 1B).

In MuSCs, the activation process is canonically characterized byexpression ofMyod1 (Grounds et al., 1992; Yablonka-Reuveni andRivera, 1994), loss of Spry1 and Pax7, and entry into the cell cycle(Shea et al., 2010). Multiple groups have characterized thedynamics of activation at the population level using ensembleassays to measure these molecular markers (Cornelison and Wold,1997; Fu et al., 2015; Jones et al., 2005; Yablonka-Reuveni andRivera, 1994; Zhang et al., 2010). Likewise, it has been reported thataged MuSCs show a delayed time to first division relative to youngcells, with fewer aged cells forming colonies in vitro (Cosgroveet al., 2014; Gilbert et al., 2010). These studies have elucidatedmany of the molecular players and sequences in MuSC activationand shown that aged cells exhibit a delay in at least one activationhallmark (first division time).

Genomics studies have revealed that MuSC activation is acomplex process, affecting many aspects of transcription and cellbehavior (Liu et al., 2013). However, it remains unknown how agingaffects the progress of activation in MuSCs outside of a small set ofmolecular markers and binary behavior features (i.e. cell cycleevents). Although it is known that aged MuSCs display a delayedcell-cycle entry, for instance, it is unknown if this one feature of cellbehavior reflects a broader delay in the activation process across the

Handling Editor: Gordon KellerReceived 16 August 2019; Accepted 17 February 2020

1Eli and Edythe Broad Center for Regenerative Medicine, University of California,San Francisco, 35 Medical Center Way, San Francisco, CA 94143, USA. 2Center forCellular Construction, University of California, San Francisco, San Francisco, CA94143, USA. 3Biochemistry & Biophysics, University of California, San Francisco,San Francisco, CA 94143, USA.*Present Address: Calico Life Sciences, LLC; 1130 Veterans Blvd, South SanFrancisco, CA 94080, USA.

‡Authors for correspondence (andrew.brack@ucsf.edu;Wallace.Marshall@uscf.edu)

J.C.K., 0000-0002-8285-619X; A.B.H., 0000-0002-8045-2474; W.F.M., 0000-0002-8467-5763; A.S.B., 0000-0002-8798-7084

This is an Open Access article distributed under the terms of the Creative Commons AttributionLicense (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use,distribution and reproduction in any medium provided that the original work is properly attributed.

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https://dev.biologists.org/content/editor-bios/#kellermailto:andrew.brack@ucsf.edumailto:Wallace.Marshall@uscf.eduhttp://orcid.org/0000-0002-8285-619Xhttp://orcid.org/0000-0002-8045-2474http://orcid.org/0000-0002-8467-5763http://orcid.org/0000-0002-8467-5763http://orcid.org/0000-0002-8798-7084

many transcriptional and cell behavior features involved.Traditional molecular biology tools have also limitedinvestigation to terminal assays, such that activation dynamics insingle cells have not been directly observed. In order todisambiguate between the different paths and different ratesmodels of MuSC aging, we require single cell measurements ofactivation dynamics that capture a broad set of transcriptional andbehavioral features.Single cell analyses in the hematopoietic system identified

distinct aged and young transcriptional phenotypes (as in thedifferent paths model), and altered cell cycle kinetics (as in thedifferent rates model) (Kowalczyk et al., 2015), suggesting that bothmodels are plausible in the context of myogenic activation. Toinvestigate each of these possibilities, we use our recently developedcell-behavior analysis platform ‘Heteromotility’ (Kimmel et al.,2018) to quantify phenotypic-state dynamics during MuSCactivation in aged and young MuSCs. Multiple groups have

demonstrated the value of single cell RNA sequencing (scRNA-seq) to elucidate differences between skeletal muscle cell types anddynamic regulation of myogenic programs following injury(Dell’Orso et al., 2019; Giordani et al., 2019; The Tabula MurisConsortium et al., 2018). We likewise complement our behavioralassay approach with scRNA-seq to map the transcriptional statespace of MuSC activation. Leveraging RNA velocity analysis (LaManno et al., 2018), we infer transcriptional-state transitiondynamics to pair with state transition dynamics inferred from cellbehavior.

In these transcriptional assays, we further investigate differencesacross age and activation state within the subsets of highlyregenerative label-retaining cells (LRCs) and less regenerativenon-label retaining cells (nonLRCs). We previously describedLRCs and nonLRCs as discrete populations of MuSCs withdifferent proliferative histories and different regenerative potentials(Chakkalakal et al., 2012, 2014). The relative proportions of these

Fig. 1. Aged MuSCs display lower cell motility and delayed activation by single cell behavior analysis. (A) Experimental schematic. MuSCs were isolatedand imaged on a time-lapse microscope for 48 h. Tracking was performed from 10 h to 35 h. (B) Diagram of the different paths and different rates models forage-related decline in muscle stem cell regenerative capacity. (C) t-SNE visualization of cell behavior state space with color overlay of hierarchical clusteringidentities (aged animals: n=742 cells; young animals: n=1201 cells). (D) t-SNE visualization of cell ages in cell behavior space. (E) Aged cells display a significantpreference for less-motile behavior clusters (χ2 test, age × behavior cluster contingency table, P

populations changes with age, suggesting that age-related changesspecific to the LRC or nonLRC compartment may shed light onMuSC aging.We find that both behavioral and transcriptional-state spaces are

continuous across MuSC activation and that measurements of cellheterogeneity are comparable between assays. In aged MuSCs, wefind aberrant transition dynamics that lead to significantly delayedactivation by both methods. These findings are reflected in acomparison of LRCs to less regenerative nonLRCs, suggestingaberrant transition dynamics might be related more generally toimpaired regenerative potential. Identifying genetic pathways thatare altered in both aged MuSCs and nonLRCs, we find thatbiosynthetic processes activate more slowly in both populations. Todetermine if less regenerativeMuSCs occupy different steady states,we trained machine-learning classifiers to discriminate MuSC ageand LRC status. Classifiers readily discriminate between MuSCages and proliferative histories. Our results suggest that aged stemcells display delayed activation kinetics, in addition to subtledifferences in the position of activation states.

RESULTSActivation kinetics are delayed in aged MuSCsPreviously, we demonstrated that quantitative measurements of cellmotility behavior from time-lapse imaging data are sufficient toresolve states of MuSC activation and state transitions (Kimmelet al., 2018). This approach allows for the direct observation of cell-state transitions during MuSC activation. Cell behaviormeasurements also have functional relevance in MuSCs, asmotility is necessary for MuSCs to translocate to sites of injury.We applied this cell behavior analysis method to aged and young

MuSCs to determine (1) if aged cells occupied distinct behavioralstates, and (2) if aged cells exhibit different cell state transitiondynamics during activation. MuSCs were isolated from aged(20 months old, n=1) and young (3 months old, n=1) mice byfluorescence-activated cell sorting (FACS). To ensure the ratio ofdistinct proliferative histories (Chakkalakal et al., 2012, 2014) inyoung and aged cells was well represented, we isolated cells afterlabeling proliferative history developmentally, and sampledrepresentative young and aged cell populations in silico. Time-lapse microscopy was performed for 48 h after plating (Fig. 1A).This temporal window captures the early stages of MuSC activation,including the switch from a quiescent Pax7+/Myod1− state to aMyod1+ state (Cornelison and Wold, 1997).We quantified cell behaviors in a 10-35 h window of the time

lapse using Heteromotility (Movies 1-4). The visualization of cellbehavior state space with t-SNE (van der Maaten and Hinton, 2008)revealed heterogeneous cell behavior states, previously shown toreflect different states of MuSC activation (Fig. 1C) (Kimmel et al.,2018). Hierarchical clustering revealed three cell behavior states(colors in Fig. 1C). Behavior cluster 1 was largely immotile,behavior cluster 2 displayed limited motility behavior, andbehavior cluster 3 displayed more extensive and dramatic motilitybehaviors.Aged and young cells did not occupy distinct regions in behavioral

state space (Fig. 1D). Quantification of the proportion of aged andyoung cells in each motility state revealed that aged cells show asignificant preference for the less motile behavior cluster 1 relative toyoung cells (Fig. 1E, χ2, P

(Fig. 2C). However, single cell analysis of these markers in our largesample of MuSCs, revealed heterogeneity within both the quiescentand activated populations. Within the quiescent cell population, a

subset of Pax7+ cells occupied the opposite end of quiescent statespace from a set of Myod1+ cells. Likewise, a subset of cells in theactivated population expressed the quiescence marker Pax7+.

Fig. 2. Single cell RNA sequencing reveals heterogeneous transcriptional states during myogenic activation. (A) Experimental schematic. Two animalswere used for each age. (B) t-SNE visualization of quiescent and activated cells. 21,555 cells were recovered. (C) Overlay of myogenic regulatory genes on t-SNEplots to show that activated MyoD+ cells localize in a terminal state. (D) Definition of heterogeneous transcriptional states by unsupervised clustering.(E) Pseudotime analysis of MuSC activation, correctly recapitulating the sequence of ground truth time points. (F) Hierarchical clustering identifies fourpatterns of pseudotemporal gene expression during MuSC activation. (G) Visualizing myogenic regulatory gene expression across pseudotime reveals that Pax7does not decrease monotonically.

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Given this heterogeneity, we utilized unsupervised Louvaincommunity detection to identify subpopulations within quiescentand activated cells (Blondel et al., 2008). Tuning the communityresolution parameter yielded four transcriptional clusters. Twotranscriptional clusters were identified within the quiescent andactivated cell populations, respectively (Fig. 2D).

Non-monotonic gene expression patterns are present inmyogenic activationWe sought to determine how these transcriptional clusters aretemporally ordered during activation using pseudotime analysis(Qiu et al., 2017). Pseudotiming revealed a distinct sequentialordering for these transcriptional clusters (Fig. 2E). Myogenic genelevels within each of the clusters corroborated the pseudotiminginference with known myogenic biology (Fig. S2B; Table S2).Transcriptional cluster 1 exhibited the highest levels of quiescencemarker Spry1, and was appropriately selected as the root of thepseudotime axis. Likewise, transcriptional clusters 3 and 4, from theactivated time point, expressed lower levels of the quiescencemarkers Pax7, Spry1 and Cd34, and expressed higher levels of theactivation marker Myod1. Across all clusters, there were no distinctcell-cycle states observed, consistent with reports of the firstdivision occurring ≥24 hours after our 18 h time point (Gilbertet al., 2010; Rodgers et al., 2014) (Fig. S2D).Pseudotime analysis placed transcriptional cluster 4 as the

endpoint of the progression, despite the fact that it contains asubpopulation of Pax7+ cells, whereas cluster 3 does not (Fig. 2C).This challenges the traditional dogma that Pax7 levels decreasemonotonically with MuSC activation and suggests a more complextemporal regulation of Pax7 (Fig. 2G). Analysis of the meanexpression in each cluster confirmed non-monotonic changes inPax7 with activation (Fig. S3A,B). Our data are consistent withprevious reports of decreased Pax7 with activation at the ensemblelevel (Cornelison and Wold, 1997; De Micheli et al., 2020;Dell’Orso et al., 2019), when we consider only the mean expressionof Pax7 at the quiescent and activated time points (Fig. S3C). Whencomparing Pax7+ cells in transcriptional cluster 4 with Pax7− cellsin the same cluster, we found that Pax7+ cells showed significantupregulation of Id1, Id2 and Id3, which inhibit differentiation (Jenet al., 1992), and of the cell-cycle gene Ccnd1 (Fig. S3D). Theseresults are consistent with the idea that Pax7+ cells might be moreproliferative (Zammit et al., 2006) and less committed than theirPax7− counterparts.By contrast, the quiescence marker Spry1 (Shea et al., 2010)

displayed a monotonic decrease with activation and Myod1displayed a monotonic increase (Fig. 2G). Previous functionalstudies with Pax7 overexpression constructs in MuSCs reported thatPax7 promotes proliferation in certain contexts (Zammit et al.,2006), consistent with increased Pax7 as cells enter into cycle laterin the activation process. We observed that the commitment markerMyog was expressed in only 25 cells in our data set, as expected forthis early time point. For this reason, we cannot estimate an accuratepseudotemporal curve (Fig. S4A).Clustering genes into modules based on pseudotemporal

expression patterns revealed that some genes exhibit monotonicincreases or decreases with activation, whereas others display non-monotonic behavior (Fig. 2F; Table S3). For example, many genesin modules 1 and 3 displayed maximum expression at points inbetween the most quiescent and most activated states. Module 1contained genes related to mRNA processing and splicing, asdetermined by gene ontology analysis. Module 3 contained genesrelated to cell cycle regulation and developmental processes (Fig.

S4B). By contrast, most genes in module 2 decreased monotonicallyacross pseudotime, and most genes in module 4 monotonicallyincreased across pseudotime.

As an orthogonal method to confirm cluster ordering andestablish a link between the transcriptional clusters and behavioralclusters, we performed immunostaining following single cellbehavior measurements. We found that the most motile and mostactivated cell behavior states are enriched for Pax7 protein (Fig. 3).Pax7+/MyoG+ cells were rare in this assay, as expected (Fig. S5).This analysis also indicated a non-monotonic regulation for Pax7across cell behavior states, as inferred from the ordering oftranscriptional clusters.

These results support the notion that transcriptional programsduring myogenic activation exhibit a variety of temporal behaviors,including non-monotonic and nonlinear temporal regulation.Expression peaks and valleys in the non-monotonically-regulatedgene modules provide evidence that there are intermediarytranscriptional states of myogenic activation that are not simpleinterpolations of the initial and final transcriptional states.

We next identified markers of myogenic activation bydifferential expression between the quiescent and activated cellpopulations. Differential expression analysis revealed 3864 genesaltered by activation. Gene ontology (GO) analysis of thedifferentially expressed marker genes suggests that these geneslargely reflect biosynthetic and metabolic pathways, consistentwith the notion that myogenic activation corresponds to a dramaticmetabolic and geometric rearrangement of cellular state (Fig. S2C).

This interpretation was further reinforced by weighted genecorrelation network analysis (WGCNA) (Langfelder and Horvath,2008), which elucidated two gene modules during activation(Fig. S6A). The first ‘quiescence module’ was upregulated inquiescent cells and contains genes related to cell stress responses,transcriptional suppression, and negative regulation of cellproliferation, as indicated by GO analysis (Fig. S6B). By contrast,the ‘biosynthetic module’ was upregulated during activation andcontains genes related to protein biosynthesis, transcriptionalupregulation, ribosome biogenesis, and RNA maturation(Fig. S6C). Together, these results suggest that myogenic activationis heterogeneous among individual cells at multiple time points in theprocess, and that the activation process can be decomposed into a setof activation states reflecting cellular biosynthetic activity.

AgedMuSC transcriptomes showmodest differences acrossmany transcriptsAged MuSCs have significantly impaired regenerative capacity. Wepreviously found that the conversion of highly regenerative LRCs toless regenerative nonLRCs is a factor in this regenerative decline(Chakkalakal et al., 2012, 2014). Do aged MuSC transcriptomesreflect these functional deficits? To answer this question, werandomly sampled populations of aged and young MuSCs withphysiological ratios of LRCs:nonLRCs to mimic MuSC poolsin vivo (Fig. S7G).

Similar to our cell behavior analysis, we found that aged MuSCsdo not segregate discretely in transcriptional space (Fig. 4A).Furthermore, we found high correlation between the geneexpression profiles of young and aged MuSCs (Fig. 4E). There isno apparent preference in aged cells among the activatedtranscriptional clusters (χ2 test, P>0.9). This differs from the statepreference of aged cells among the behavioral clusters we identified(Fig. 1D), suggesting that either the state preference arises after the18 h time point captured by scRNA-seq, or that the state preferenceis less dramatic at the transcriptional level.

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We used differential expression analysis to identify 174differentially expressed genes between aged and young cellswhen comparing both quiescent and activated cells (Fig. 4B;Table S4). GO analysis of these genes indicated that they representbiosynthetic processes and stress responses, with protein translationprocesses upregulated with aging and stress responsesdownregulated (Fig. 4C). Among the differentially expressedgenes, young cells displayed elevated levels of the stress responseheat-shock proteinsHspb1 andHspa5, suggesting that agedMuSCsare less able to mount appropriate stress responses.We also found differentially expressed genes with aging

specifically within the quiescent and activated states. Inquiescence, we found 200 differentially expressed genes betweenaged and young MuSCs (Fig. S7A; Table S5). GO analysissuggested that these genes are related to protein folding and cellularresponses to environmental stresses. Both of these processes are

downregulated in aged cells (Fig. S7B). During activation, weidentified 275 differentially expressed genes, suggesting thatactivation accentuates age-related transcriptional differences (Fig.S7C; Table S6). GO analysis likewise identified that these genes areassociated with catabolic processes (downregulated in aging), andstress responses (upregulated in aging) (Fig. S7D). Two of the topdifferentially expressed genes are associated with increases inlongevity (Ladiges et al., 2009; Swindell, 2011).

Gene expression variance is altered by agingn addition to information about mean gene expression levels, singlecell RNA-seq provides information about the variation inexpression within cell populations. Recent work has suggestedthat aging might increase gene expression variance in immune cells(Martinez-Jimenez et al., 2017; Kolodziejczyk et al., 2015). Wefound that aged cell gene expression is more variable in the

Fig. 3. Pax7 is non-monotonicallyregulated across MuSC cellbehavior states during activation.(A) Experimental design schematic.MuSCs were isolated, time-lapse imagedin culture for 36 h, and subsequentlyimmunostained. Behavior traces andimmunostaining results were matched foreach cell by image registration. (B) t-SNEvisualization of behavior clusters inmotility state space, as defined byhierarchical clustering. Behavior statespacewas generated by analyzing 12 h oftracking data, from 24 h after isolation to36 h (n=1003 cells, 1 animal). (C) Pax7immunostaining intensity (cell median)and binary frequency within each cellbehavior cluster. Both quantificationschemes show a non-monotonicrelationship between behavioralactivation state and Pax7 intensity (95%confidence intervals from 1000 bootstrapsamples). (D) Representative images ofPax7 and MyoG staining in MuSCs aftertime-lapse imaging for behavior analysisin each behavior cluster. Panels on the farleft are the final DIC image from the timelapse, registered and overlaid withfluorescent immunostains. Remainingpanels are fluorescent images prior toregistration. Fluorescence images areequitably rescaled across cells withineach channel for presentation.

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quiescent state (P

approach, we used support vector machine (SVM) models with asparsity penalty to classify aged and youngMuSCs by incorporatinginformation from multiple genes. We identified a set of candidate‘chrono-variant’ genes that change with age, focusing on the case ofactivatedMuSCswhere differences are more pronounced (Fig. 4F,G).We first applied this machine-learning approach to predict age

within LRC and nonLRC subsets at each time point. Classifyingactivated LRC age, we identified a set of 54 genes that yield aprediction accuracy of ∼85% (holdout validation; Table S6)(Fig. 4H). For nonLRC classification, we identified 104 genesthat yield a predictive accuracy of ∼96% (Fig. 4H; Table S6;Fig. S9). Of these genes, only 40 are common to both LRCs andnonLRCs, suggesting that transcriptional aging manifestsdifferently in LRC and nonLRC populations. Classification of theactivated young and aged MuSC pools (each sampled to modelphysiological LRC:nonLRC ratios) identified a set of 99 genes thatprovide ∼95% classification accuracy (Fig. 4H; Table S7). In eachcase (LRC, nonLRC and pooled), we found that classification ofactivated cells is more effective than classification of quiescentcells, further suggesting that activation reveals transcriptional agingphenotypes. This classification result represents the first effectiveassay to discriminate the age of individual muscle stem cells.

Estimating the contribution of LRC to nonLRC conversion totranscriptional agingThe proportion of LRCs in the MuSC pool is ∼35% in younganimals and decreases to ∼15% with age. To estimate thecontribution of this cell state conversion to the overall changes weobserve with age, we estimated the ‘magnitude’ of age-relatedchanges using a classification-based density-ratio estimationmethod (Sugiyama et al., 2011). Populations of aged and youngcells were simulated by random sampling with eitherphysiologically observed LRC:nonLRC ratios or equal LRC:nonLRC ratios. The overall difference between young and agedcells did not change significantly across these conditions(Fig. S10A,B). The similarity in classification accuracy anddivergence magnitude, in the face of changes to the LRC:nonLRCratio, suggests that LRC-to-nonLRC conversion does not dramaticallyalter the overall ‘magnitude’ of age-related transcriptional change.

Activation manifests transcriptional differences due toproliferative historyLRCs are functionally distinct from nonLRCs, and this functionaldifference persists throughout life (Chakkalakal et al., 2012, 2014).Although we found that the majority of transcriptional differencesbetween aged and young cells are independent of proliferativehistory, we also investigated whether LRCs and nonLRCs weretranscriptionally distinct from one another. Similar to aged andyoung cells, LRCs and nonLRCs appear to share a transcriptionalstate space (Fig. 5A). We first investigated expression of knownregulators and markers of the myogenic state in LRCs and nonLRCs.We found that themajority ofmyogenicmarkers are not differentiallyexpressed between LRCs and nonLRCs in this assay (Fig. 5B).Likewise, the mean gene expression levels between the two stateshave a near perfect correlation in quiescent cells (r=0.99; Fig. 5C).However, Myod1 is upregulated in LRCs after activation but not

in quiescence (Fig. 5B). Regression analysis of mean geneexpression levels between the LRC and nonLRC states revealed anear-perfect correlation in quiescent cells, but in activation the meangene expression values were less correlated (P

separate model to classify LRC/nonLRCs at each time point andeach age. Classification of quiescent cells performs poorly at bothages (∼65% accuracy), whereas classification of activated cells ismore effective – ∼85% accuracy for young cells and ∼80% for agedcells (Fig. 5E; Fig. S10C). As in our age classification experiments,this result suggests that activation reveals differences between cellpopulations that are masked in quiescence. Our regularized modelsidentified 102 genes that optimize LRC/nonLRC classification ofyoung activated cells, and 72 genes that optimize classification ofaged activated cells (Table S14).

Transcriptional kinetics are aberrant in aged MuSCsThe lack of unique aged transcriptional states and modestdifferential expression results between aged and young cells aresurprising in light of the dramatic differences in functional potentialbetween aged and young cell populations (Chakkalakal et al., 2012;Cosgrove et al., 2014). These results suggested to us that the rate at

which aged and young cells activate might be an additional sourceof variation that contributes to their functional differences. Toquantify rates of phenotypic change between the MuSCtranscriptional states during activation, we utilized the recentlydeveloped RNA velocity method (La Manno et al., 2018). Thismethod estimates a ‘velocity’ of transcription, or rate of change inthe transcript level, by estimating mRNA decay rates andtranscription rates using ratios of spliced to unspliced reads.

RNA velocity estimation showed that each state of MuSCtranscription gave rise to a neighboring state in the sequenceinferred by pseudotiming (Fig. 6A). As an internal validation check,we found that RNA velocity indicated that quiescent cells weremoving toward activated cells in transcriptional space.

The magnitude of mean RNA velocity represents the rate ofcollective phenotypic change at the transcriptional level for a givengroup of cells. This approach provides an inferred measurement ofstate transition rates in transcriptional space, similar to the

Fig. 5. Activation induces differential responses in LRCs and nonLRCs. (A) LRCs and nonLRCs labeled in a t-SNE visualization of transcriptional space.(B) Expression of known myogenic regulatory genes in quiescent and activated LRCs and nonLRCs. Larger dots indicate a greater proportion of expressingcells, darker colors indicate higher expression. Only Myod1 differences in activation are significant (q

measurement of state transition rates we make by direct observationin cell behavior space. Quantification of the magnitude of RNAvelocity across pseudotime in MuSCs revealed that RNA velocityfollows a concave curve (Fig. 6C).Concave transition rates are consistent with a model in which the

peak transition rate represents a ‘switch’ between two states,corroborating our earlier observations made by cell behavioranalysis (Kimmel et al., 2018). We note that the switch-like processsuggested by these results might be unique to our in vitro culturesetting, and does not necessarily reflect in vivo activation kinetics.Consistency in state transition measurements between RNA velocityand cell behavior phenotyping suggests that cell behavior statetransitions reflect the underlying transcription state kinetics.Do aged and young MuSCs move through transcriptional state

space differently? To answer this question, we employed a methodto model cellular progression through transcriptional space usingphase point simulations. Phase point analysis is a dynamicalsystems method to investigate the properties of a vector field inwhich a simulation is performed to determine how a particle might

flow along a vector field, as if it were a floating leaf carried bycurrents in a river (Strogatz, 2015). Here, we simulated a set of phasepoints that begin in the more primitive regions of our ‘activated’18 h time point and evolved them over time using velocities inferredfrom either young or aged cells (Fig. 6B). Given these simulatedtrajectories through transcriptional space, we investigated whethernotable differences are present in phase points simulated usingyoung velocities (‘young phase points’) relative to those simulatedusing aged velocities (‘aged phase points’).

To assess progress through cell activation, we trained ak-nearest neighbors regression model (kNN-R) to maptranscriptome principal component analysis (PCA) embeddingsto pseudotime coordinates as determined using Monocle 2(Figs S5; S12A,B). For each time step in a phase pointsimulation, we predicted a pseudotime coordinate of the pointusing this model. Comparing these inferred coordinates, youngphase points progressed more rapidly through the activationprocess than aged phase points (Fig. 6D). After many time steps,both young and aged phase points reached similar inferred

Fig. 6. Aged MuSCs transition aberrantly through transcriptional space. (A) MuSC transcriptional space overlaid with arrows representing the direction andmagnitude of RNA velocity at each state location (20,827 cells). Colors indicate transcriptional clusters. (B) Representative phase point simulations in agedand young RNA velocity fields, overlaid on the activated MuSC cells in a PCA embedding. (C) State transition rates as measured by RNA velocity magnitudeacross pseudotime using a rolling mean. (D) Predicted pseudotime progression for phase point simulations in either aged (red) or young (blue) velocity fields.Young phase points progress more rapidly than aged cells. Curves cross when young and aged phase points have both reached a steady-state at the end of ourobserved pseudotime trajectory. n=1000 phase points simulated for each age. (E) Change in pseudotime for phase point simulations at each time step.(F) Heatmap representing the mean density of phase points at each point in principal components space across the entire simulation. Young and aged phasesimulations show qualitatively similar trajectories through state space. (G) Terminal locations of young and aged phase point simulations in principal componentsspace, overlaid on cell locations. Both young and aged simulations show similar final resting positions.

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pseudotime locations as they neared the edge of our observedpseudotime trajectory (Fig. 6D).The computation of numerical derivatives for pseudotime

coordinates, Δpseudotime, showed that young phase pointsappeared to progress more rapidly from the earliest time steps(Fig. 6E). This result suggests that aged cells might progress moreslowly than young cells through the activation process in a similarmanner to the phase point simulations. Results were robust toinitialization conditions and the introduction of noise (Fig. S12C).Phase point simulations provide additional information about the

location of ‘attractors’ in transcriptional space. Attractors arelocations in a state space where phase points tend to converge andcome to rest. Visualization of the density of phase points intranscriptional space as the number of phase points to pass through aregion, showed there were qualitatively few differences in the shapeof trajectories between young and aged cells (Fig. 6F; Movie 5).Focusing on the locations where phase points come to rest, there arelikewise modest differences in the specific shapes of attractor states,but overall similar attractor positions between young and aged phasepoint simulations (Fig. 6G).Collectively, results of these phase point simulations suggest that

the set of intermediate transcriptional states a MuSC visits in thecourse of activation is largely similar between young and aged cells.However, aged cells appear to activate at a slower rate than youngcounterparts. Each of these points supports the different rates modelof aging pathology outlined above.

Lineage regression occurs during myogenic activation in asubset of MuSCsThe discovery of reserve cells generated during myogeniccommitment more than 20 years ago first presented the idea thatMuSCs might revert to earlier stages in the lineage progressionunder some conditions (Yoshida et al., 1998). It is currently unclearhow frequently MuSCs transition ‘backwards’ in the myogenicactivation program. We assessed the frequency of MuSCstransitioning backward in the lineage progression by quantifying a‘change in pseudotime’ (Δpseudotime) for each cell in our youngMuSC single cell RNA-seq data set. Δpseudotime was estimatedusing the kNN-R model referenced above. Pseudotime values werepredicted for the ‘future’ transcriptomes inferred by RNA velocity,and the difference between the predicted pseuodotime and observedpseudotime values was taken as the Δpseudotime. We defined a cellas ‘regressing’ in pseudotime if Δpseudotime was more than half astandard deviation below 0.This analysis reveals that ∼16% of young MuSCs are regressing

in pseudotime during the period of myogenic activation we measure

(Fig. 7A). Regression is more frequent in activated (∼20%) thanquiescent (∼15%) MuSCs. Quantification of the frequency of‘lineage regression’ across pseudotime for cells from our later timepoint (18 h in vitro) revealed that cells regress more frequently in thelater stages of activation we observed (Fig. 7B). RNA velocity is ameasure of instantaneous change in the cell state, such that theseresults do not necessarily suggest a subset of cells that permanentlyfails to activate. Rather, these results suggest that myogenicactivation is a two-way process even under growth-promotingconditions, perhaps resembling a biased random walk throughtranscriptional space.

This regression behavior appears to be robust to age-relatedchanges (Fig. 7C; Fig. S13A). When comparing LRCs withnonLRCs, LRCs regress less frequently than nonLRCs in youngand aged animals (Fig. S13B,C; χ2 test, P

nature to investigate muscle stem cell activation in vitro. Wesurprisingly found minor transcriptional differences between agedand young MuSCs at a given point in the activation process,consistent with previous reports (Cosgrove et al., 2014; Keyes et al.,2016; Sousa-Victor et al., 2014). Although transcriptionaldifferences with age have been observed previously, it remainedunknown if these differences were a large enough source ofvariation to alter the trajectory of myogenic activation. Our singlecell RNA-seq data allows us to observe that age-related changes aremuch smaller than changes between states of activation, such thatthe trajectory of activation is preserved with aging.By measuring stem cell state transitions directly, we observed that

aged MuSCs have dampened state transition rates. By behavioralanalysis, aged MuSCs displayed a preference for less-motile, less-activated states and decreased rates of transition into more activestates. Similarly, phase point analysis of RNA velocity vectorssuggests that aged cells transition more slowly throughtranscriptional states during the earliest phases of activation thanyoung counterparts. These data support a conceptual model inwhich aging MuSCs exhibit ‘different rates’ of activation, even ifthey follow the same trajectory. These differences in activation ratesuggest that some transcriptional differences between young andaged MuSCs observed at the ensemble level may be the result ofdifferences in the distribution of cells across the activationtrajectory.Our data also reveal heterogeneity in the stem cell activation

process. Ensemble read-outs during MuSC activation havedemonstrated that MuSCs occupy different transcription factorstates, even at a single time point (Cornelison and Wold, 1997).However, these measurements could not explain where in theactivation process heterogeneity arose. Here, we find that MuSCsprogress through the activation process stochastically, with anontrivial proportion of the population moving ‘backwards’through the activation process. This suggests that the heterogeneityof MuSC positions along the activation trajectory arises as anaccumulation of differences in the rate of cell state transitions.These differences appear to be both stochastic and associated

with distinctive features between MuSC subpopulations, such asproliferative history. Although the macroscopic processes of muscledevelopment and regeneration proceed without these apparentreversals, these observations indicate that phenotypic change at thecellular level might involve considerably more noise. This isreminiscent of the qualitative differences between the physicalmotion of macroscopic objects, like a ball rolling down a hill, andmicroscopic motion, where noise can dominate the movement ofsmall molecules, which often reverse direction completely.The in vitro model we employ allows us to assess cell-intrinsic

differences between young and aged MuSCs with a homogeneousactivation stimulus, but may not fully recapitulate the in vivobiology. For instance, although we do not observe bistabletranscriptional states between the most-quiescent and most-activated cells in our experiments, stable intermediary activationstates might exist in vivo (Rodgers et al., 2014). Future work mayexplore stem cell activation in vivo to determine if cell-extrinsicchanges with age exacerbate or dampen the differences in activationwe observe between young and aged MuSCs. In vivo imagingtechnologies for MuSCs are nascent (Webster et al., 2016), buttechnological advances may allow for similar quantitative cellbehavior analysis in vivo in the future. Future work may also explorethe molecular determinants of stem cell activation rates to enabletherapeutic rescue of MuSC activation dynamics in the agedcontext.

MATERIALS AND METHODSAnimalsAnimals were handled according to the University of CaliforniaSan Francisco (UCSF) Institutional Use and Care of Animals Committee(IUCAC) guidelines. All experimental mice were maleMus musculus of theC57Bl/6J background. All mice were born at UCSF and aged in-house. Allmice for single cell sequencing experiments harboredH2B-GFP+/−; rtTA+/−

alleles and were developmentally labeled for proliferative history by theadministration of doxycycline between embryonic day (E) 10.5 and E16.5(Chakkalakal et al., 2014; Foudi et al., 2009). Aged mice for RNA-seqsequencing experiments were 20 months old and young micewere 3 monthsold. For one behavior experiment (Fig. 1), one young (3 months old) andone aged (20 months old) H2B-GFP+/−; rtTA+/− mouse with LRCs labeleddevelopmentally as above, were used as sources of young and aged MuSCs,respectively. We cultured LRCs/nonLRCs in separate wells after FACSsorting and reconstituted observed young (35% LRC) and aged (15% LRC)proportions by random sampling in silico for downstream analysis. For thesecond behavior experiment (Fig. S1), we isolated cells from wild-typeyoung (5 months old) and aged (23 months old) mice. Adult (3-4 monthsold) Pax3-GFP/+ reporter mice were used for MuSC activation experiments(Relaix et al., 2005).

Cell isolation and cultureMuscle stem cells were isolated by FACS using a triple-negative CD31−/CD45−/Sca1− and double-positive VCAM+/α7-integrin+ strategy asdescribed previously (Chakkalakal et al., 2012). Antibody clones, sourcesand catalog numbers are described in supplementary Materials andMethods. Cells were seeded in sarcoma-derived extracellular matrix(ECM)-coated 96-well plates in rich growth medium [F10 (Gibco), 20%fetal bovine serum (FBS) (Gibco), (5 ng/ml) FGF2 (R&D Systems)] forbehavior analysis. For single cell sequencing experiments, cells were seededin sarcoma-derived ECM-coated 6-well plates and allowed to activate inplating media [Dulbecco’s modified eagle media (DMEM), 10% horseserum] for 18 h prior to library preparation.

Cells for behavior analysis were seeded at 850 cells/well on sarcoma-derived ECM (Sigma-Aldrich) in 96-well plates. Cells were maintained inrich growth media [F10 (Gibco), 20% FBS (Gibco), (5 ng/ml) FGF2 (R&DSystems)]. For single cell sequencing experiments in which cells wereactivated, cells were seeded in sarcoma-derived ECM-coated 6-well platesand allowed to activate in plating media (DMEM, 10% horse serum) for18 h prior to library preparation.

Time-lapse imaging and cell behavior analysisMuSCs were imaged in 96-well plates on an incubated microscopy platform(Okolab) for 48 h. Images were collected with DIC contrast every 6.5 min totrack cell movement (supplementary Materials and Methods). Cell behaviorwas analyzed using Heteromotility, as described previously (Kimmel et al.,2018; available at github.com/cellgeometry/heteromotility) (supplementaryMaterials and Methods).

Paired immunohistochemistry and time-lapse imagingFor paired behavior-immunocytochemistry experiments, cells were fixed in4% paraformaldehyde (PFA) for 10 min immediately following the imagingtimecourse and stained with using a standard protocol (supplementaryMaterials andMethods). After staining, cells were returned to the same time-lapse microscopy system used for behavioral imaging, and fluorescentimages were captured. We performed segmentation of the final brightfieldimage from the behavioral time course, and the nuclear channel of thefluorescence-stained image, then performed image registration using nearestneighbors to match immunofluorescence signals to cell behavior tracks.Stain intensity was quantified as the median intensity of a fluorescent signalwithin the nucleus of each cell.

EdU stainingFor LRC/nonLRC experiments, muscle stem cells were isolated as describedabove from three H2B-GFP+/−; rtTA+/− mice (4 months old, labeleddevelopmentally as above) and cultured in plating media (DMEM, 10%horse serum) for 50 h. EdU (Carbosynth) was pulsed into the media at 12 h

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and 2 h (10 µM final concentration) prior to fixation with 4% PFA for15 min. Staining followed the Click-iT EdU Alexa Fluor 647 kit protocol(Thermo Fisher Scientific). For Pax3+/Pax3− experiments, Pax3+ andPax3− MuSCs from forelimbs and pectoralis muscles were collected byFACs from Pax3-GFP/+ adult mice, plated on ECM-coated chamber-slidesand cultured in high-serum conditions (20% FBS, F10) for 42 h. EdU(10 µM) was added during the final 12 h. EdU was detected by fluorescencemicroscopy. We tested the significance of EdU incorporation using logisticregression models and the Wald test (supplementary Materials andMethods).

Single cell RNA-sequencingLRC and nonLRCMuScs were isolated by FACS based on GFP intensity,as described previously (Chakkalakal et al., 2014). Half of the collectedcells were immediately transferred to a 10x Genomics Chromium systemfor library preparation using the 10x 3′ Single Cell v. 2 chemistry. Theremaining cells were activated by in vitro cell culture (as described in the‘Cell isolation and culture’ section above), then dissociated using CellDissociation Buffer, (Gibco, 13151014) stained with propidium iodide(PI), and sorted by FACS to remove dead cells. Live activated cells weretransferred to the 10x Chromium system for identical library preparation.Libraries were pooled and sequenced using an Illumina NovaSeqplatform.

Single cell transcriptome analysisRaw sequencing data were demultiplexed using Illumina bcl2fastq.Demultiplexed sequencing reads were aligned to the mouse transcriptomeusing the STAR aligner (Dobin et al., 2013). Individual unique molecularidentifiers (UMIs) were detected and assigned to corresponding cell barcodesusing 10x Genomics cellranger, samplewise. Droplets containing cells wereidentified and individual libraries were aggregated using cellranger.

A genes×cells count matrix was generated from the aggregated libraries usingcellranger. Suspected dead cells were removed when a high proportion of totalUMIs in the cell mapped to mitochondrial genes (Ilicic et al., 2016) (>10%mitochondrial reads). Putative doublets were removed as outliers on a histogramof UMIs/cell and genes/cell (Brennecke et al., 2013) (>5000 genes/cell).Prior to normalization, the annotated transcripts Gm42418 and AY036118 wereremoved from the count matrix. These transcripts overlap an unannotated Rn45srRNA locus, and may include counts from rRNAmolecules that were amplifiedduring library preparation despite polyA-selection.

Raw counts were log normalized using Seurat (Satija et al., 2015).Variable genes were identified using ‘FindVariableGenes’ in Seurat andPCA was performed on the variable gene set. t-SNE was performed on theprincipal components with perplexity P=30. Community detection wasperformed with the Louvain algorithm (Blondel et al., 2008). Small clustersof contaminating cells without myogenic marker genes were removed fromsubsequent analysis.

Contribution of factors to transcriptional variationThe proportion of variation explained by each experimental factor in ourmulti-factor experiment was estimated with linear models as describedpreviously (Robinson et al., 2015) (supplementary Materials and Methods).We scored cell cycle states using the approach in Seurat, introduced inTirosh et al. (2016).

Overdispersion analysisOverdispersion scores were computed using the difference from the median(DM) method (Kolodziejczyk et al., 2015). We eliminated all genes with amean expression lower than µ=0.1, as technical noise for genes with verylow mean expression is known to be high (Kolodziejczyk et al., 2015)(supplementary Materials and Methods).

Estimation of LRC to nonLRC contribution to transcriptionalchangeWe estimated the ‘magnitude’ of transcriptional change with aging betweena set of young and aged transcriptomes by training probabilistic classifiersto estimate the density ratio between distributions of young and agedtranscriptomes (Rosca et al., 2017 preprint; Sugiyama et al., 2011). We used

estimated density ratios to compute a Kullback–Leibler divergence betweentwo samples (supplementary Materials and Methods).

PseudotimingPseudotime analysis was performed using the Monocle 2 package (Qiuet al., 2017). Genes for pseudotemporal ordering were determined bydifferential expression analysis between the transcriptional clusters, asdescribed in the following section. Pseudotiming was performed on all20,000+ cells that passed quality control simultaneously in the sametranscriptional state space utilizing the DDRTree method with twocomponents. We further clustered genes into gene modules basedon their pseudotemporal behavior using the Monocle 2 function‘plot_pseudotime_heatmap’. For this clustering analysis, we focused ongenes that were significantly differentially expressed across pseudotime(q0.96 performance). To determine differences betweenaged and young velocity fields, phase point simulations were performedwith numerical methods. A set of 1000 initial positions in the 2D PCAembedding for both young and aged cells was sampled from observedcellular positions in the primitive region of the activated time point. Phasepoint positions were evolved for t=5000 time steps, using a k-nearestneighbors approach to compute a velocity estimate and update point positionat each iteration (supplementary Materials and Methods). For simulations inyoung and aged velocity fields, only young or aged cells, respectively, wereconsidered at this step.

Change in pseudotime analysisThe ‘change in pseudotime’ (Δpseudotime) was estimated for each cellusing the kNN-R (supplementary Materials and Methods). Futuretranscriptional states xt+1 were inferred by RNA velocity as above, and thepseudotimes for these states were predicted using the kNN-R model.ΔPseudotime is defined as the difference between the inferred future andmeasured present pseudotime for each cell:

Dp ¼ p̂tþ1 � pt ,

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where p̂tþ1 is the inferred pseudotime using RNA velocity and the kNN-Rmodel and pt is the observed pseudotime at the experimental time point.Cells were defined to be undergoing ‘lineage regression’ if they displayed aΔpseudotime

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