
==== Front
101573691
39703
Cell Rep
Cell Rep
Cell reports
2211-1247

39102335
10.1016/j.celrep.2024.114593
nihpa2019537
Article
Transcriptional noise, gene activation, and roles of SAGA and Mediator Tail measured using nucleotide recoding single-cell RNA-seq
Schofield Jeremy A. 12*
Hahn Steven 13*
1 Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
2 Present address: MilliporeSigma, Rockville, MD, USA
3 Lead contact
AUTHOR CONTRIBUTIONS

Conceptualization, methodology, investigation, formal analysis, writing – original draft, visualization, J.A.S.; supervision, writing – review & editing, funding acquisition, S.H.

* Correspondence: jschofie@fredhutch.org (J.A.S.), shahn@fredhutch.org (S.H.)
11 9 2024
27 8 2024
04 8 2024
17 9 2024
43 8 114593114593
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).
SUMMARY

We describe a time-resolved nascent single-cell RNA sequencing (RNA-seq) approach that measures gene-specific transcriptional noise and the fraction of active genes in S. cerevisiae. Most genes are expressed with near-constitutive behavior, while a subset of genes show high mRNA variance suggestive of transcription bursting. Transcriptional noise is highest in the cofactor/coactivator-redundant (CR) gene class (dependent on both SAGA and TFIID) and strongest in TATA-containing CR genes. Using this approach, we also find that histone gene transcription switches from a low-level, low-noise constitutive mode during M and M/G1 to an activated state in S phase that shows both an increase in the fraction of active promoters and a switch to a noisy and bursty transcription mode. Rapid depletion of cofactors SAGA and MED Tail indicates that both factors play an important role in stimulating the fraction of active promoters at CR genes, with a more modest role in transcriptional noise.

In brief

Schofield and Hahn use nascent single-cell RNA-seq to investigate transcriptional noise and gene activation. Only a subset of genes shows high mRNA variance suggestive of transcription bursting. Cofactors SAGA and Mediator Tail play an important role in stimulating the fraction of active promoters, with a modest role in transcriptional noise.

Graphical Abstract
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pmcINTRODUCTION

Eukaryotic protein-coding genes can be transcribed either constitutively, characterized by stochastic uncorrelated transcription initiation events, or in bursts of initiation separated by stochastic on/off periods.1–3 Bursting is described by several kinetic parameters that can vary depending on the gene and chromatin environment. These parameters include the frequency of transition to the active promoter state, the duration of active periods, and the number of initiation events during the active period (burst size).4 The frequency of activation is generally thought to be controlled through enhancer activity in metazoans,5 while the size and variance of bursting have been attributed to core promoter elements. Both transcription factors and chromatin regulators can regulate the timing and magnitude of transcriptional bursts in yeast.6,7 DNA supercoiling, generated by transcription of neighboring genes, can also affect bursting behavior and may couple the behavior and variance of neighboring genes.8,9 Thus, genes can differ considerably in their bursting behavior resulting from the contribution of regulatory factors, gene sequence, and the physical chromatin environment.10,11

Although “burstiness” is not easily described by a single kinetic term, bursty gene expression is characterized by large quantities of RNA produced in a single cell over a short time leading to high mRNA variance per active cell, referred to as intrinsic transcriptional noise12,13 (Figure 1A). Transcriptional heterogeneity is most prominent for genes in stress response pathways and cancer cells.14,15 The question of how bursting relates to gene regulation has been explored in metazoans, where studies found that the modulation of promoter activation frequency is the dominant form of regulation.16 However, subsets of genes can also be regulated through modulation of the burst size.17,18 High-throughput gene knockout studies using reporter genes paired with live-cell microscopy demonstrated that human transcription factors and coactivators promote distinct transcriptional kinetic behaviors.19 At select genes, measurements of gene-specific transcription kinetics using live-cell microscopy have illuminated roles for specific regulators and DNA sequence contexts associated with variance in bursting,3,20 but the generality of bursting behavior and gene activation mechanisms for most genes remains an open question.

In most systems studied, TATA-containing promoters are often associated with high variance, noise, and bursting.21–23 One interpretation of this finding is that TATA promotes retention of a stable “scaffold complex” after RNA polymerase II escape (a subset of basal transcription factors bound to the promoter), leading to rapid reinitiation and bursting.21,24 However, the finding that TATA-containing genes display higher transcriptional noise may be confounded by the fact that other gene properties overlap with TATA, including high expression25 and gene-specific regulation by transcription cofactor complexes, including SAGA26,27 and Mediator (MED) Tail.28,29

In yeast, several gene classes have been defined based on their cofactor dependence (Figure 1A). The cofactor (or coactivator)-redundant (CR) gene class (CR genes; ~13% of protein-coding genes)26,27 are partially dependent on both SAGA and TFIID, and rapid codepletion of these factors leads to a severe defect in transcription. CR genes are enriched for stress response genes, and ~60% of CR gene promoters contain a TATA. The CR gene class can be further divided into two subclasses based on MED Tail dependence, the activator-binding domain of yeast MED. ~44% of CR genes are sensitive to rapid depletion of the MED Tail module (Tail-dependent [TD] genes), and ~70% of these TD gene promoters contain TATA.28,29 CR MED Tail-independent genes (TI genes) are insensitive to rapid depletion of MED Tail.29 Conversely, the remaining 87% of genes (TFIID-dependent genes) are enriched for housekeeping genes, only ~20% of these promoters contain TATA, and transcription decreases upon rapid TFIID (but not SAGA) depletion.26

Transcriptional cofactors display specificity for UAS sequences in yeast30 and enhancers in metazoans,31,32 and despite the observed connection between enhancers and transcription activation frequency, the roles of cofactors in gene-specific transcriptional bursting behavior have not been studied systematically. Current limitations on studying gene-specific bursting for large numbers of genes include the low throughput of microscopy approaches and the lack of time resolution in standard single-cell RNA sequencing (scRNA-seq) data. However, recent advances in transcriptome metabolic labeling methodology33,34 and scRNA-seq35,36 now permit time-resolved estimates of transcription in single cells.

To address transcriptional variance and the fraction of active promoters and how these properties are affected by cofactors on a genome-wide scale, we describe a time-resolved scRNA-seq (nucleotide recoding [NR]-scRNA-seq) approach to measure transcription over short time periods across the yeast transcriptome (Figure 1B). NR approaches allow for the simultaneous measurement of nascent and pre-existing RNAs, which can be leveraged to estimate steady-state transcript half-lives as well as capture the number of newly made RNAs over time.37,38 Using this approach, we found that transcriptional noise behavior is associated with specific cofactor regulatory class genes as well as specific promoter elements. We also investigate changes in promoter behavior upon gene activation and how rapid depletion of SAGA and MED Tail alter genome-wide transcription properties. Our approach should be adaptable to many cell types.

RESULTS

Genome-wide measurement of transcription noise and Fon using NR-scRNA-seq

To measure transcriptional parameters for the set of expressed protein-coding genes, we adapted scRNA-seq to include metabolic RNA labeling and NR chemistry (NR-scRNA-seq) (Figure 1B). We performed a time course of 4-thiouracil (4TU) metabolic labeling (5, 10, and 15 min) in yeast cells undergoing log growth, treated cells with alkylation chemistry, and prepared droplet-based RNA-seq libraries (10× Genomics). The NR-seq approach modifies 4TU nucleobases in newly transcribed RNAs, resulting in a T-to-C “mutational” (recoding) signature in cDNA derived from new RNAs. To determine the efficacy of NR, we first analyzed single-cell datasets in bulk. We used a Bayesian modeling approach (bakR38) to estimate the T-to-C NR rate in nascent vs. pre-existing RNAs. The estimated recoding rate in nascent transcripts for labeled samples ranges from 4.7% to 5.7%, while the background mutation rate in pre-existing RNAs is 0.04%. Using a mixed-binomial approach to estimate the fraction of newly transcribed RNAs over the label period,33,38 we determined the half-lives of thousands of poly(A) transcripts. Transcript half-lives estimated from our three independent time points (Table S1) are highly reproducible (Figure S1A), and the median half-life of 8.2 min agrees well with prior estimates in yeast and displays the known correlation with codon optimality (Figures 1C and S1B). To gain an understanding of the frequency of promoter activation for yeast genes, we next estimated the fraction of cells actively expressing each gene (Fon) across the time points and found a global increase in median Fon over time (Figures 1D and S1C). Due to the low number of new RNA observations for lowly expressed transcripts, our Fon measurements are likely underestimates. Despite this, our Fon estimates are highly reproducible between independent experimental runs (Figure S2) and scale appropriately with metabolic labeling time, validating the consistency in temporal information gained in this approach.

To analyze transcriptional noise strength for thousands of yeast genes, we estimated the number of new RNAs synthesized over time per gene and per cell using a binomial probability approach (see STAR Methods; Figures S1C and S3). We then used these new RNA count estimates to calculate the mean-normalized variance (Fano factor) of new RNAs across many cells. Assuming a Poisson process where transcription initiation events are independent, we expect variance to be equal to the mean (Fano = 1). A Fano factor greater than 1 indicates higher variance than the mean and a non-independent relationship between transcription initiation events. As expected, Fano captures the variance in mRNA abundance observed for different genes as revealed by NR-scRNA-seq (Figure S4A), and genes with high Fano are associated with transcription bursting.39 Importantly, Fano estimates agree well across time points and experimental strains (Figure S4B). Strikingly, most genes display relatively low Fano levels (median Fano = 1.19, n = 2,968), with a minority of genes displaying exceptionally high Fano (Fano >2, n = 18), suggesting a high level of transcriptional bursting (Figure 2A). Bootstrapping analysis demonstrates that genes with exceptionally high Fano levels are clearly separable in their Fano values from the 95% confidence estimates of most low-Fano-level genes (Figure S4C). This is consistent with prior results, where most yeast genes assayed displayed constitutive behavior (low variance in RNA and protein) with a subset of genes displaying high variance.3,20,40,41 From our results, many high variance genes are highly expressed, although even among highly expressed genes, we observe a wide range of Fano values, suggesting that variance is not related to expression level alone (Figure 2B; Table S2). CR genes, on average, display higher Fano values than TFIID genes across 3 of 4 expression quartiles (Figure 2B), and we found that CR genes containing TATA boxes generally display higher Fano than CR genes without TATA boxes (Figure 2C). However, TATA is not the only important feature, as CR genes containing TATA boxes show higher variance than TFIID genes with TATA boxes (Figure 2C), suggesting that factors regulating CR transcription also play an important role in promoting high variance behavior. Interestingly, MED TD genes have comparable Fano whether or not they contain a consensus TATA (Figure 2D); however, nearly all TD promoters lacking a consensus TATA contain a closely related TATA derivative.30 Finally, we observe that promoters with long residence times of the basal transcription factor TFIIF,42 presumably due to a longer-lived activated state, display, on average, higher Fano values (Figure S4D), providing a connection between our measured transcriptional variance and an independent kinetic analysis of basal-factor-promoter stability. Together, our results indicate that, on a genome-wide scale, both gene class and promoter elements contribute to transcriptional noise.

Activation of cell-cycle-regulated genes

We next assessed whether transcription activation affects Fon and whether gene-specific noise is intrinsic to the promoter or varies by the activation state of the gene. For this analysis, we examined the activation of cell-cycle-specific genes. We first grouped cells from our 10 min 4TU dataset into cell cycle states based on new RNA counts using k-means clustering on a set of 297 curated cell-cycle-regulated genes43 (STAR Methods). Although clustering will not precisely define the cell cycle state, and there is some temporal overlap between cell cycle states due to the 10 min metabolic labeling time, this approach successfully recapitulates known cell-cycle-dependent transcriptional regulation based on transcript abundance (Figure 3A). We found that generally, genes whose expression levels peak in each cell cycle stage also display the highest Fon for that corresponding cell cycle stage (Figure S5A), consistent with the previously observed connection between gene expression output and promoter activation frequency. We then compared the Fon to Fano for genes at each stage of the cell cycle to assess how mRNA variation changes under differing activation states. Consistent with prior knowledge, we observe variation in Fon for histone genes (Figure 3B, top) increasing in G1 phase, peaking in S phase, and decreasing in G2 phase. Interestingly, we observe high levels of variance (Fano) in G1, S, and G2 cells compared to the relatively low levels of variance in M and M/G1 cells (Figure 3B, bottom). Although histone transcription is tightly cell cycle regulated, we observe ~15%–20% of cells actively transcribing histone genes during the M and M/G1 cell cycle stages, suggesting that histone genes are transcribed but are in a less active state during these latter cell cycle stages. In addition, non-cyclic genes display relatively uniform levels of Fano across the cell cycle (Figure S5B), suggesting that the high variance state for histone genes in G1, S, and G2 cells is not influenced by global changes across the cell cycle. Our combined results suggest that the histone genes are transcribed at a low level in a low-noise constitutive mode during M and M/G1, while the activated state is characterized by an increased Fon as well as switching to a noisy and bursty transcription mode in G1, S, and G2 (Figure 3C).

We then assessed whether the degree of cell cycle regulation leads to differences in transcriptional noise. We binned genes according to the degree of cell cycle regulation43 and found that both CR genes and TFIID genes with low cell cycle regulation (quartile 1) display similarly low Fano (Figure 3D). However, for genes with high cell cycle regulation (quartile 4), CR genes display significantly higher Fano than TFIID genes, suggesting that a high-variance activated state is a feature of the highly regulated CR gene class. Finally, we examined the connection between Fon and variance, comparing cells in M/G1 phase vs. cells in G1 phase. We observed the strongest correlation between Fon and Fano for the CR TD genes (R2 = 0.38) (Figure S5C). Although these changes appear to be occurring in a small number of genes, the results are consistent with a high-variance activated state similar to our observations in the histone genes.

Effects of cofactor depletion on transcription parameters

Since we observed differences in transcriptional variance across gene regulatory classes, we interrogated the gene-specific effects on noise and gene activation after rapid depletion of transcription cofactor subunits using the auxin-inducible degron system.44 For this analysis, we consider transcriptional “burstiness” to be associated with high mean expression per active cell (MPC) and high transcriptional variance (Fano) and gene activation to be associated with the fraction of cells actively transcribing specific genes over the labeling period (Fon). We interrogated the role of the SAGA complex by simultaneously depleting core subunits Spt3 and Spt7 for 30 min prior to NR-scRNA-seq and the role of the MED Tail by depleting the activator-binding subunit Med15 for 30 min. We confirmed the depletion of coactivator subunit proteins using western blot analysis of the V5 epitope on IAA7 degron-tagged Med15 and Spt3/7 (Figure S6A). We theorized that depletion of cofactors would lead to changes in the fraction of cells expressing specific CR target genes and/or the mean RNA expression per active cell (MPC) (Figure 4A). We analyzed the log2-fold change in bulk transcription as well as Fano, MPC, and Fon after the rapid depletion of SAGA or MED Tail. Following Med15 depletion, we observe a decrease in Fon, MPC and Fano for MED TD genes (Figure S6B) and a slight increase in these three parameters for MED TI genes (both CR and TFIID genes). Following Spt3/7 depletion (Figure S6C), we observe defects in Fon, MPC, and Fano for CR genes (both MED TD and TI) and in Fon and Fano for TFIID genes. Examining genome-wide changes following Med15 and Spt3/7 depletion (Figures 4B and 4C), we observe a strong correlation (R2 = 0.93, 0.94; linear model fit) between changes in bulk transcription and Fon, and modest correlations (R2 = 0.34, 0.22) between changes in bulk transcription and MPC. Consistent with our hypothesis of MPC and Fano being indicative of transcriptional bursting, we observe moderate correlations (R2 = 0.57, 0.62) between the two parameters upon MED Tail and SAGA depletion. These trends are consistent when considering only MED TD genes following Med15 depletion (Figure S7A), as well as CR genes (Figure S7B) and TFIID genes (Figure S7C) following Spt3/7 depletion. Taken together, our observations support a model of MED Tail and SAGA function playing an important role in stimulating the frequency of gene activation (reflected in Fon), with a modest role in transcriptional bursting behavior (reflected in MPC and Fano).

In addition to the above analysis of cofactor dependence, it was also informative to examine the behavior of other gene classes and specific genes, as we found examples of gene-specific changes in Fon, MPC, and Fano following cofactor depletion. We first focused our analysis on histone genes following Tail or SAGA depletion. Yeast histones genes are chromosomally arranged in pairs as divergent tandem promoters. While all histone genes are in the CR gene class and dependent on SAGA,26 only two of the four pairs of histone genes are dependent on MED Tail.29 Following Tail depletion, we observed defects in bulk transcription and Fon for the TD, but not the TI, histone genes (Figure 5A). Similarly, the TD histone genes are generally more defective in MPC and Fano than the TI genes. Following SAGA depletion, we observe defects in all four parameters for all histone genes, and these defects are largely comparable between TD and TI genes. These results illustrate how transcriptional regulators play distinct roles at different subsets of genes, which can affect transcriptional output and variance.

To further illustrate the gene-specific effects of cofactors, we examined changes in parameters following cofactor depletion for specific non-histone genes. For ribosomal biogenesis genes, SAGA depletion generally results in little effect on bulk transcription and the three measured parameters (Figure S8A), while Tail depletion generally results in a slight increase in both bulk transcription and all measured parameters (Figure S8B). However, for ribosomal protein (RP) genes, SAGA depletion results in a decrease in transcription, MPC, and Fon, with a slight decrease in Fano (Figure S8A). Conversely, MED Tail depletion results in an increase in bulk transcription and MPC, with a substantial increase in Fano (Figure S8B). These results are consistent with prior observations of transcriptional stimulation of RP genes upon Tail depletion.45

We further analyzed changes following cofactor depletion by comparing changes in bulk transcription, Fon, MPC, and Fano at two specific high-expression, CR and MED TD genes. We hypothesized that depletion of cofactors at these CR and MED TD genes may lead to gene-specific changes in transcriptional parameters, depending on factors including core promoter elements. Depletion of either MED Tail or SAGA results in large changes in bulk transcription for the GAS1 gene (Figure 5C), and these changes appear to be primarily driven by changes in Fon. For the AHP1 gene, SAGA depletion results in changes to both Fon and MPC but little change in Fano, whereas Tail depletion results in larger decreases in both MPC and Fano with smaller changes to Fon (Figure 5D). Interestingly, GAS1 contains a TATA-less promoter, while AHP1 contains a promoter with a TATA box, further strengthening the connection between cofactor complexes, core promoter elements, and transcriptional bursting. Our combined results demonstrate that, on average, MED Tail and SAGA function display a strong connection to Fon and a modest connection to MPC and Fano, although their relative roles in promoting these transcriptional behaviors can vary substantially among affected genes.

DISCUSSION

Here, we present the results of a time-resolved scRNA-seq approach to study transcription parameters related to noise and gene activation in yeast cells. We demonstrate that temporal information gained through this approach is consistent across increasing time courses and comparing samples from the same time point across experiments. Consistent with prior work, we found that most yeast protein-coding genes display relatively low levels of transcriptional variance, while a small subset of genes display exceptionally high variance. We also observe that gene features, including core promoter sequence (e.g., TATA boxes) and transcriptional cofactor class, correlate with higher Fano values, which agrees well with observations using microscopy data on select model yeast genes.3 Fano is generally higher for CR genes compared to TFIID genes across expression quartiles and is especially high for cell-cycle-regulated CR genes.

Current models of gene activation state that generally, the primary mechanism by which transcription is controlled is via modulation of the fraction of active promoters over time. In higher eukaryotes, this has been studied in the context of enhancer-promoter communication, in which an enhancer functions in part to increase the frequency of target promoter activation. An alternative (though not mutually exclusive) form of regulation is modulation of the bursting properties of an already active promoter, regulating the amount of transcription per cell by controlling the duration of the active state and/or the frequency of initiation during the active state. Our results suggest that both of these mechanisms operate for the cell-cycle-regulated histone genes that appear to switch transcription from a low-level constitutive mode in M and M/G1 to an activated and bursty mode in G1, S, and G2. In our cofactor depletion experiments, we found that the change in the fraction of active promoters closely correlates with the bulk transcriptional fold change, consistent with the model where SAGA and Med Tail modulate promoter activation frequency. We do, however, observe modest changes in mean expression per active cell (MPC) (a measure of transcription produced during the active state) after MED Tail or SAGA depletion, suggesting that both MED and SAGA can play a modest role in modulating the bursting properties of an already active promoter. Furthermore, the relative changes in Fon vs. MPC can vary widely by gene, suggesting that, mechanistically, these cofactor complexes may play different roles in promoting transcription in a gene-dependent fashion. Although we cannot directly address this hypothesis using our dataset, it is conceivable that these cofactors may support more frequent or longer-lived activation periods at some genes and/or more transcription per active period at others. Further experimentation using a method with higher time resolution (e.g., live-cell microscopy) will be necessary to test these hypotheses.

Limitations of the study

Due to limitations in capturing the efficiency of RNAs using scRNA-seq approaches and metabolic labeling efficiency, our estimates of Fon are likely underestimates. In addition, we are not able to distinguish multiple potential activation periods during the metabolic labeling period. For future studies, higher temporal precision may be achieved using recently developed single-cell run-on approaches (scGRO-seq).46

STAR★METHODS

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Steven Hahn (shahn@fredhutch.org).

Materials availability

All unique reagents generated in this study are available by request from the lead contact without restriction.

Data and code availability

High-throughput sequencing data is deposited in the Gene Expression Omnibus under accession number GSE247795.

Scripts used to process datasets in this manuscript are publicly available on GitHub via the following link: https://github.com/jaschofield/single_cell_NR_seq.

Information required to reanalyze the data reported in this paper is available from the lead contact upon request.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Yeast strains are derivatives of S. cerevisiae BY470547 and construction of these strains is described in Donczew et al. 202026 and Warfield et al. 202229 and are listed in the key resources table.

METHOD DETAILS

Time course

WT yeast cells (BY4705/SHY772) were grown to mid-log phase (OD ~0.8) and treated with 5 mM 4-TU for 5 min, 10 min, or 15 min or left untreated (DMSO only). Immediately following 4-TU treatment, cells were placed on ice and 2 volumes of ice-cold PBS was added to the culture in 50 mL Falcon tubes. Cells were pelleted for 5 min at 3000×g in a benchtop centrifuge at 4°C in a swinging bucket rotor and washed with cold PBS +0.01% BSA. Cells were pelleted at 3000×g at 4°C for 5 min and resuspended in 1 mL PBS +0.01% BSA. Cells were fixed by continuous gentle vortexing in a 50 mL Falcon tube while adding ice-cold methanol dropwise to a final concentration of 80%. Cells were incubated on ice for 15 min, at which point fixed cells were stored overnight at −20°C. The following day, fixed cells were incubated on ice for 15 min, then pelleted for 5 min at 3000×g in a benchtop centrifuge at 4°C. Cells were resuspended in 1 mL ice-cold PBS +0.01% BSA, transferred to a 1.5 mL Eppendorf tube and pelleted at 2000×g for 1 min. Cells were resuspended in 400 μL PBS +0.01% BSA, and the cell suspension was added to 1 mL of alkylation buffer (per 1 mL: 700 μL DMSO, 140 μL 500 mM sodium phosphate pH 8, 140 μL 100 mM iodoacetamide, 20 μL water), followed by a 45 min incubation at 45C with occasional inversion. Cells were pelleted at 2000×g for 30sec and resuspended in 1mL PBS +0.01% BSA +10mM DTT. This reducing wash step was repeated for a total of two washes, followed by pelleting at 2000×g for 30 s and resuspension of the pellet in 1M sorbitol +0.01% BSA. Cells were pelleted at 2000×g for 30 s, and cells were then resuspended in 900 μL spheroplasting buffer (per 1 mL: 500 μL 2M sorbitol, 350 μL water, 100 μL 1% BME, 20 μL 500 mM EDTA pH 8, 20 μL 500 mM sodium phosphate pH 8, 10 μL 10 mg/mL BSA), and 100 μL of 100 mg/mL Zymolyase 100T was added. Cells were spheroplasted for ~30 min at 30°C with occasional inversion. The spheroplasting mixture was filtered through a MACS SmartStrainer (Miltenyi Biotec)and collected in a 1.5 mL Eppendorf tube on ice, and residual spheroplasts were rinsed through the SmartStrainer using 500 μL ice-cold spheroplasting buffer. Spheroplasts were pelleted for 5 min at 2000×g forming a cushion of spheroplasts toward the bottom of the Eppendorf tube. Supernatant was removed leaving ~100 μL of spheroplast cushion, which was diluted 1:5 in ice-cold 1M sorbitol +0.01% BSA. Spheroplasts were counted using a hemocytometer immediately prior to Gel Beads in emulsion (GEM) generation.

Coactivator depletion

Coactivator depletion studies were performed in Med15-degron (SHY1055) or Spt3/7-degron (SHY1176) strains. Prior to 4-TU labeling for 10 min, cells were treated with either 500 μM 3-indoleacetic acid (IAA) in DMSO or DMSO alone as a negative control, and the remainder of the above protocol was performed.

Western blot analysis

1 mL of each Med15-degron and Spt3/7-degron cell cultures were collected immediately after treatment with IAA or DMSO. Cells were pelleted, then resuspended in 200 μL 0.1M NaOH and incubated for 5 min at room temperature. Cells were pelleted, then resuspended in 100 μL yeast whole cell extract buffer (0.06M Tris-HCl, pH 6.8, 10% glycerol, 2% SDS, 5% 2-mercaptoethanol, 0.0025% bromophenol blue) and heated for 5 min at 95C. Samples were centrifuged for 5 min at max speed, then extracts were subjected to SDS-PAGE using a precast NuPAGE 4%–12% Bis-Tris gel using MOPS running buffer. Proteins were transferred onto a PVDF membrane, and after transfer the membrane was cut at the ladder band (SeeBlue Plus2 pre-stained protein standard) corresponding to 62 kDa. A mouse monoclonal primary α-V5 antibody (Thermo Fisher) was used to probe Med15–3xV5-IAA7, Spt3–3xV5-IAA7 and Spt7–3xV5-IAA7 fusion proteins, and rabbit polyclonal primary α-Tfg2 antibody was used to probe endogenous Tfg2 as a loading control. Dye-conjugated mouse or rabbit secondary antibodies were used to visualize antibody-bound proteins, and dye signals were captured using an Odyssey CLx scanner.

Library preparation and sequencing

GEM generation and single cell RNA-seq library preparation was performed using the Chromium Next GEM Single Cell 3′ Kit from 10X Genomics according to manufacturer’s instructions. Prepared libraries were sequenced on an Illumina NextSeq 2000 instrument using P3 reagents, with read configuration of 28nt for R1 to identify cell barcode and UMIs, and 125nt for R2 to sequence the captured mRNA.

Sequence processing

Single cell IDs were determined from sequencing data using the default CellRanger (7.1.0) analysis pipeline (count), using a yeast transcriptome built from 3′-end annotations.56 Sequencing alignment and T-to-C recoding counting was performed using the TimeLapse-seq computational pipeline (https://bitbucket.org/mattsimon9/timelapse_pipeline/src/master/, v0.4). Custom scripts were used to assign T-to-C recoded-counted reads with their cells of origin using the cell barcodes in the CellRanger output.bam file. For the time course datasets with higher numbers of input cells, potential doublet cells were identified and filtered using doubletFinder_v3.

Estimation of transcript half-lives

To determine transcript half-lives, we first pooled T-to-C recoded-called reads from our single cell experiments to create a pseudo-bulk datasets. From these pooled reads, we estimated the fraction of new RNAs per gene over each labeling period using a Bayesian modeling approach (bakR,38 1.0.1). bakR was used to estimate per-sample and background (from no 4TU-treated control) T-to-C recoding rates, then was used to fit per-gene fraction new for each timepoint using a maximum likelihood estimation (fast_analysis). Transcript half-lives were calculated assuming first-order rate kinetics, using the following equation57: t1/2=−t×ln(2)/ln(1−fnew)

where t is the labeling time and fnew is the estimated fraction new calculated from the above Bayesian analysis.

Estimation of per gene per cell new RNA counts

To account for potential background mutations in sequencing, nascent read counts were simulated from the subset of T-to-C mutation containing reads using a binomial probability approach. Each read was assigned a probability of being newly transcribed based on the global sample recoding rate, number of Ts in the read, and number of T-to-C mutations in the read. Each read was then subjected to a random draw according to its binomial probability to assign it as new or pre-existing. Simulated new reads were then summed per-gene and per-cell to generate the final new read count matrix.

Fano calculation

For each gene, the mean (m) and variance (standard deviation2; σ2) of estimated new read counts across cells were calculated. Fano was then calculated according to the following equation: Fano=σ2/m.

Estimation of per gene fraction on (Fon)

To estimate the fraction of cells active over the labeling period for each gene, we estimated gene-specific T-to-C recoding rates for all reads in each single cell by summing the total number of T-to-C mutations per gene per cell and dividing by the total number of Ts observed per gene per cell. We then assigned a cell as actively expressing a given gene over the label period if this recoding rate exceeded 10-fold above the background mutation rate. This cutoff was determined by examining the distribution of per gene per cell mutation rates in the non-4TU labeled NR-seq dataset (where the Fon for all genes is expected to equal zero) and applying varying cutoffs. We found that a 10-fold background mutation rate minimizes Fon estimates for the non-4TU control, especially for high expression genes. Final Fon estimates were calculated by dividing the number of active cells per gene by the total number of cells.

Cell cycle assignments

Cells were assigned to a cell cycle state through k-means clustering of new read counts of highly cell cycle regulated genes. Briefly, a matrix of new RNA counts for genes vs. cells was filtered to contain only cell cycle regulated genes with a high periodicity score determined in Spellman et al.43 (n = 297). k-means clustering was performed to group cells into five clusters, corresponding to G1, S, G2, M, and M/G1 cell cycle stages.

QUANTIFICATION AND STATISTICAL ANALYSIS

For scRNA-seq time course datasets, single replicates were performed for each time point (t = 0 min, 5 min, 10 min, 15 min) in the WT yeast strain (SHY772). For scRNA-seq coactivator depletion datasets (Med15-degron SHY1055 strain and Spt3/7-degron SHY1176 strain), single replicates were performed for IAA-treated and DMSO-treated (negative control) samples. p-values for mean comparisons displayed in boxplots and violin plots were determined using a Wilcoxon test, and these values were determined using R software package ggpubr.55 Significance levels are defined as: ****p ≤ 0.0001, ***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05, ns p > 0.05. The details of quantification are included in the following sections of the STAR Methods: “sequence processing”, “estimation of per gene per cell new RNA counts”, “estimation of transcript half-lives”, “estimation of per gene fraction on (Fon)”, and “cell cycle assignments”. The details of statistical analysis applied are included in the following sections of the STAR Methods: “estimation of transcript half-lives”, “fano calculation”.

Supplementary Material

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ACKNOWLEDGMENTS

We thank Manu Setty, Dan Larson, and members of the Hahn and Tsukiyama labs for their insightful comments on this work. We also thank Fred Hutch Genomics for assisting in sample preparation and consulting on project development, especially Elizabeth Jensen and Dolores Covarrubias. This work was supported by NIH grants R35 GM140823 to S.H. and P30 CA015704 to the Fred Hutch Genomics and Computational Shared Resources Facility. The graphical abstract was created with BioRender.com.

Figure 1. NR-scRNA-seq approach to measure transcription in yeast cells

(A) Top: model depicting the roles of transcriptional coactivators (TFIID, SAGA, and Mediator with Tail domain) at yeast genes. Highlighted are three coactivator gene classes and their enrichment for core promoter TATA boxes. Bottom: model depicting differing modes of transcriptional noise behavior, the expected observed mean RNAs per active cell (MPC), and the fraction of cells expressing the measured gene (Fon).

(B) Metabolic labeling scheme, cell fixation, nucleotide recoding (NR) chemistry, and single-cell RNA-seq (scRNA-seq) approach to measure mean new RNAs per active cell (MPC), gene expression variance (Fano), and fraction of cells expressing each gene (Fon).

(C) Distribution of poly(A) transcript half-lives calculated from bulk fraction-new mRNA estimates derived from the 10 min labeling time point.

(D) Estimates of per-gene fraction on (Fon) for 5, 10, and 15 min labeling time points. Statistic shown is Wilcoxon rank-sum test.

Created in part with BioRender.com. Data in (C) and (D) are single biological replicates for each condition shown.

See also Figures S1–S3.

Figure 2. Genome-wide Fano values determined by NR-scRNA-seq

(A) Rank order plot of calculated Fano values for yeast genes (n = 2,968). Genes are colored by coactivator class,26 with selected high Fano genes labeled.

(B) Boxplot displaying Fano values (outliers not plotted) comparing CR and TFIID genes across expression quartiles. Statistic shown is Wilcoxon rank-sum test; the number of genes in each category is displayed as “n.”

(C) Boxplot displaying Fano values (outliers not plotted) comparing CR and TFIID genes with and without promoter TATA boxes.26 Statistic shown is Wilcoxon rank-sum test, and the number of genes in each category is displayed as “n.”

(D) Boxplot displaying Fano values for CR genes (outliers not plotted) comparing MED Tail-dependent and MED Tail-independent genes29 with and without promoter TATA boxes.26 Statistic shown is Wilcoxon rank-sum test, and the number of genes in each category is displayed as “n.”

For (A)–(D), significance levels are defined as ****p ≤ 0.0001, ***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05, and ns p > 0.05. Data in (A)–(D) are single biological replicates for the 10 min wild-type (WT) sample.

See also Figure S4.

Figure 3. Fano and Fon values measured across the yeast cell cycle

(A) New RNA count matrix for cells assigned to differing cell cycle stages (grouped, y axis) based on k-means clustering of cyclic genes. Genes are grouped on the x axis by approximate cell cycle state of peak expression.43

(B) Fon estimates (top) and Fano estimates (bottom) for histone gene expression in cells assigned to differing cell cycle stages.

(C) Scheme depicting Fon and Fano trends for yeast histone genes across the cell cycle.

(D) Fano estimates for CR vs. TFIID class genes26 divided into quartiles based on cell cycle regulation score.43 Statistic shown is Wilcoxon rank-sum test.

For (A)–(D), significance levels are defined as ****p ≤ 0.0001, ***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05, and ns p > 0.05. Created in part with BioRender.com. Data in (A), (B), and (D) are single biological replicates for the 10 min WT sample.

See also Figure S5.

Figure 4. Genome-wide changes in transcriptional properties following acute coactivator depletion

(A) Scheme depicting potential defects in MPC and/or Fon for target genes across cells following MED Tail or SAGA depletion.

(B) Pairwise comparisons between log2-fold changes (L2FCs) in bulk transcription, Fon, MPC, and Fano following acute depletion (30 min) of MED Tail (Med15 subunit). R2 values shown are adjusted R-squared values derived from linear model fits.

(C) Pairwise comparisons as in (B) but with measurements after acute depletion of SAGA (Spt3/7 subunits).

Created in part with BioRender.com. Data in (B) and (C) are single biological replicates for each condition shown. See also Figures S6–S8.

Figure 5. Gene-specific changes in transcriptional properties following coactivator depletion

(A and B) L2FC in bulk transcription, Fon, MPC, and Fano following (A) Med15 depletion or (B) Spt3/7 depletion for the eight histone genes. MED Tail-dependent genes are shown in purple, while MED Tail-independent genes are shown in teal.

(C and D) L2FC in bulk transcription, Fon, MPC, and Fano for (C) GAS1 and (D) AHP1 genes following Spt3/7 or Med15 depletion.

Data in (A)–(D) are single biological replicates for each condition shown.

KEY RESOURCES TABLE REAGENT or RESOURCE	SOURCE	IDENTIFIER	
	
Antibodies			
	
Mouse monoclonal anti-V5	Invitrogen, Thermo Fisher Scientific	Cat #R960-25; AB_2556564	
Rabbit polyclonal anti-Tfg2	Hahn lab	Rabbit #260K	
	
Chemicals, peptides, and recombinant proteins			
	
3-Indoleacetic acid (3-IAA)	Sigma-Aldrich	Cat #I3750; CAS: 87-51-4	
4-thiouracil	Sigma-Aldrich	Cat #440736; CAS: 591-28-6	
Iodoacetamide	Sigma-Aldrich	Cat #I1149; CAS: 144-48-9	
Zymolyase 100T	MP Biomedicals	Cat #08320932	
	
Critical commercial assays			
	
Chromium Next GEM Single Cell 3' Kit v3.1	10X Genomics	Cat# PN-1000268	
Chromium Next GEM Chip G Single Cell Kit	10X Genomics	Cat # PN-1000127	
Dual Index Kit TT Set A	10X Genomics	Cat # PN-1000215	
	
Deposited data			
	
Raw and analyzed scRNA-sequencing data	This Work	GEO: GSE247795	
	
Experimental models: Organisms/strains			
	
S. cerevisiae strains are derivatives of BY4705	Brachmann et al., 199847	N/A	
S. cerevisiae wild-type strain Genotype: mat alpha delta ade2:hisG his3 delta 200 leu2 delta 0 lys2 delta 0 met15 delta 0 trp1 delta 63 ura3 delta 0	Hahn Lab	BY4705/SHY772	
S. cerevisiae Spt3/7-degron strain Genotype: mat alpha delta ade2:hisG his3 delta 200 leu2 delta 0 lys2 delta 0 met15 delta 0 trp1 delta 63 ura3 delta 0 RPB3-3x Flag:NAT MX pGPD1-OSTIR::HIS3 SPT7-3xV5 IAA7:KanMX SPT3-3xV5 IAA7degron:URA3	Hahn Lab	SHY1176	
S. cerevisiae Med15-degron strain Genotype: mat alpha delta ade2:hisG his3 delta 200 leu2 delta 0 lys2 delta 0 met15 delta 0 trp1 delta 63 ura3 delta 0 RPB3-3x Flag:NAT MX pGPD1-OSTIR::HIS3 MED15-3xV5 IAA7:KanMX	Hahn Lab	SHY1055	
	
Software and algorithms			
	
R version 4.4	The R Project for Statistical Computing	https://cloud.r-project.org/	
TimeLapse-seq pipeline	Matthew Simon Lab	https://bitbucket.org/mattsimon9/timelapse_pipeline/src/master/	
Cell Ranger v7.0.1	10X Genomics	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/7.0/release-notes	
Tidyverse v2.0.0	Wickham et al.48	https://cran.r-project.org/web/packages/tidyverse/index.html	
Rsamtools v2.20.0	Morgan et al.49	https://bioconductor.org/packages/release/bioc/html/Rsamtools.html	
Purr v1.0.2	Wickham et al.50	https://cran.r-project.org/web/packages/purrr/index.html	
Seurat v5	Hao et al.51	https://cloud.r-project.org/web/packages/Seurat/index.html	
DoubletFinder	McGinnis et al.52	https://github.com/chris-mcginnis-ucsf/DoubletFinder	
bakR v1.0.1	Vock and Simon38	https://cran.rstudio.com/web/packages/bakR/	
Pheatmap v1.0.12	Kolde53	https://cran.r-project.org/web/packages/pheatmap/index.html	
Boot v1.3-30	Canty and Ripley54	https://cran.r-project.org/web/packages/boot/index.html	
ggpubr v0.6.0	Kassambara55	https://rpkgs.datanovia.com/ggpubr/	

Highlights

Method developed to probe genome-wide transcription noise and promoter activation

Only a subset of yeast mRNAs shows high variance suggestive of bursting

Transcription properties dependent on cofactor response class and promoter type

SAGA and MED Tail function primarily to stimulate the fraction of active promoters

DECLARATION OF INTERESTS

The authors declare no competing interests.

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.celrep.2024.114593.
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