Biology subjects

Franks, A.

Publications and source records attributed to Franks, A..

5 recordsLinked to original sources

bioRxiv2025

Principles of protein abundance regulation across single cells in a mammalian tissue

Protein synthesis and clearance are major regulatory steps of gene expression, but their in vivo regulatory roles across the cells comprising complex tissues remains unexplored. Here, we systematically quantify protein synthesis and clearance across over 4,200 cells from a primary tissue. Through integration with single-cell transcriptomics, we report the first quantitative analysis of how individual cell types regulate their proteomes across the continuum of gene expression. Our analysis quantifies the relative contributions of RNA abundance, translation, and protein clearance to the abundance variation of thousands of proteins. These results reveal a putative organizing principle: The contributions of both translation and protein clearance are linearly dependent on the cell growth rate. Further, we find that some proteins are primarily regulated by one mechanism (RNA abundance, translation, or clearance) across all cell types while the dominant regulation of other proteins is cell-type specific. Age related changes in protein abundance are cell-type specific and correlated to changes in protein clearance. Our reliable multimodal measurements enabled quantifying and functionally interpreting molecular variation across single cells from the same cell type. The protein-protein correlations are substantially stronger than the mRNA-mRNA ones both for directly interacting proteins and for functional protein sets. This difference is mediated by protein clearance regulation. Further, the protein correlations allow identifying cell-type specific functional clusters. These clusters vary across cell types, revealing differences in metabolic processes coordination, partially regulated by protein degradation. Our approach reveals organizing principles determining the relative contributions of translation and protein clearance and provides a scalable framework for investigating protein regulation in mammalian tissues.

systems biology↗

bioRxiv2024

Inferring post-transcriptional regulation within and across cell types in human testis

Single-cell tissue atlases commonly use RNA abundances as surrogates for protein abundances. Yet, protein abundance also depends on the regulation of protein synthesis and degradation rates. To estimate the contributions of such post transcriptional regulation, we quantified the proteomes of 5,883 single cells from human testis using 3 distinct mass spectrometry methods (SCoPE2, pSCoPE, and plexDIA). To distinguish between biological and technical factors contributing to differences between protein and RNA levels, we developed BayesPG, a Bayesian model of transcript and protein abundance that systematically accounts for technical variation and infers biological differences. We use BayesPG to jointly model RNA and protein data collected from 29,709 single cells across different methods and datasets. BayesPG estimated consensus mRNA and protein levels for 3,861 gene products and quantified the relative protein-to-mRNA ratio (rPTR) for each gene across six distinct cell types in samples from human testis. About 28% of the gene products exhibited significant differences at protein and RNA levels and contributed to about 1, 500 significant GO groups. We observe that specialized and context specific functions, such as those related to spermatogenesis are regulated after transcription. Among hundreds of detected post translationally modified peptides, many show significant abundance differences across cell types. Furthermore, some phosphorylated peptides covary with kinases in a cell-type dependent manner, suggesting cell-type specific regulation. Our results demonstrate the potential of inferring protein regulation in from single-cell proteogenomic data and provide a generalizable model, BayesPG, for performing such analyses.

systems biology↗

bioRxiv2021

An aging clock using metabolomic CSF

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid from healthy human subjects, and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimers and Parkinsons disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small dataset (n = 85), and discuss techniques for drift correction, missing data imputation, and regularized regression which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of cerebrospinal fluid.

systems biology↗

bioRxiv2020

Testing the adaptive value of sporulation in budding yeast using experimental evolution

Saccharomyces yeast can grow through mitotic vegetative cell division while they convert resources in their environment into biomass. When cells encounter specific low nutrient environments, sporulation may be initiated and meiotic division produces 4 haploid cells contained inside a protective ascus. The protected spore state does not acquire new resources but is partially protected from desiccation, heat, and caustic chemicals. Because cells cannot both be protected and acquire resources simultaneously, committing to sporulation represents a trade-off between current and future reproduction. Recent work has suggested that one of the major environmental factors that select for the formation of spores is passaging through insect guts, as this also represents a major way that yeasts are vectored to new food sources. We subjected replicate populations of a panel of 5 yeast strains to repeated, predictable passaging through insects by feeding them to fruit flies (Drosopila melanogaster) and then allowing surviving yeast cell growth in defined media for a fixed period of time. We also evolved control populations using the same predictable growth environments but without being exposed to flies. We assayed populations for their sporulation rate, as measured by the percentage of cells that had sporulated after resource depletion. We found that the strains varied in their ancestral sporulation rate such that domesticated strains had lower sporulation. During evolution, all strains evolved increased sporulation in response to passaging through flies, but domesticated strains evolved to lower final levels of sporulation. We also found that exposure to flies led to an evolved change in the timing of the sporulation response relative to controls, with a more rapid shift to sporulation, and that wild-derived strains showed a more extreme response. We conclude that strains that have lost the ability to access genetic variation for total sporulation rate and the ability to respond to cues in the environment that favor sporulation due to genetic canalization during domestication.

evolutionary biology↗

bioRxiv2018

DART-ID increases single-cell proteome coverage

Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). This method implements global retention time (RT) alignment to infer peptide RTs across experiments. DART-ID then incor-porates the global RT-estimates within a principled Bayesian framework to increase the confidence in correct peptide-spectrum-matches and decrease confidence in incorrect peptide-spectrum-matches. Applying DART-ID to hundreds of monocyte and T-cell samples pre-pared by the Single Cell Proteomics by Mass Spectrometry (SCoPE-MS) design increased the number of data points by 30 - 50% at 1% FDR, and thus decreased missing data. Quantification benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for downstream analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://github.com/SlavovLab/DART-ID. Author SummaryIdentifying and quantifying proteins in single cells gives researchers the ability to tackle complex biological problems that involve single cell heterogeneity, such as the treatment of solid tumors. Mass spectrometry analysis of peptides can identify their sequence from their masses and the masses of their fragment ion, but often times these pieces of evidence are insufficient for a confident peptide identification. This problem is exacerbated when analyzing lowly abundant samples such as single cells. To identify even peptides with weak mass spectra, DART-ID incorporates their retention time - the time when they elute from the liquid chromatography used to physically separate them. We present both a novel method of aligning the retention times of peptides across experiments, as well as a rigorous framework for using the estimated retention times to enhance peptide sequence identification. Incorporating the retention time as additional evidence leads to a substantial increase in the number of samples in which proteins are confidently identified and quantified.

genomics↗