Biology subjects

Quake, S.

Publications and source records attributed to Quake, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Translation fidelity mechanisms and their changes in aging

Protein errors (amino acid misincorporations) are associated to protein misfolding and misfunction, and lower levels correlate with longer lifespan. Despite their prevalence, proteome-wide tracking of misincorporations in eukaryotes is challenging, with most insights being recent and coming from the mining of extensive proteomics data sets. These experimental challenges have precluded the direct in vivo probing and full understanding of the bases of fidelity in higher eukaryotes, and their influence in complex processes such as aging. Here we measured proteome-wide misincorporation frequencies in two experimentally tractable eukaryotic systems, yeast and mouse, using mass spectrometry. We find translation fidelity is organism and organ specific. The measured error frequencies correlate with codon:anticodon pairing thermodynamics and tRNA pool composition, which can have synergistic or opposing effects on fidelity depending on organism or tissue type. Most identified protein errors carry a negative fitness burden. Aging experiments in our two systems reveal that, with age, error frequencies increase in post-mitotic cells, but not so in mitotic ones. These changes correlate with remodeling of the tRNA pool, which we identify as a crucial regulator of translation fidelity during lifespan.

molecular biology↗

bioRxiv2026

The molecular triggers of human labour: a longitudinal plasma proteomics study

Spontaneous labour onset is a precisely timed physiological transition that determines outcomes for millions of pregnancies annually, yet its upstream molecular triggers remain unknown. Here we report a longitudinal plasma proteomics study using the Olink Explore HT platform to measure over 5,400 protein targets in repeated blood samples (median 13 samples per woman) taken from 40 women in the last month up to spontaneous term labour and delivery. Combining longitudinal trajectory modelling with interpretable machine learning for labour timing prediction, we identified five proteins - AFP, ACTA2, ANGPT2, IL1RL1, and LMOD1 - that change in a reproducible sequential order preceding labour onset across all 40 women. IL1RL1, encoding both soluble and membrane-bound ST2, the receptors for IL-33, rose earliest and most consistently, preceding declining AFP and ANGPT2 and a late rise in the smooth muscle contractile proteins ACTA2 and LMOD1. The temporal ordering of these proteins is consistent with a two-phase biological cascade in which feto-placental maturation and vascular remodelling precede activation of the IL-33/ST2 alarmin axis, until the soluble/membrane bound ST2 (sST2/ST2L) balance shifts towards membrane-bound signaling, driving myometrial contractile priming. Mendelian randomisation provided independent human genetic support for the involvement of the IL-33/ST2 axis in labour timing. These findings provide longitudinal plasma proteomic evidence implicating activation of this axis in the timing of spontaneous human labour and identify LMOD1 as a novel circulating marker of myometrial activation.

systems biology↗

bioRxiv2026

Informational blueprints reveal condition-dependent gene regulatory architectures

While coding regions in the genome have a direct interpretation in terms of protein products, significant fractions are non-coding and yet control essential biological functions. Unlike the genetic code, there is no "lookup table" that identifies where regulatory proteins, known as transcription factors (TFs), bind. Here, we extract these binding sites by distilling sequences of nucleotide letters into collective coordinates (hyperletters) representing the binding sites that are active under specific environmental conditions. Going beyond local information footprints between individual bases and expression levels, our information blueprint algorithm compresses the global information by optimising filters that simultaneously scan an entire promoter sequence. Inspired by renormalisation-group techniques, we identify TF binding sites as coarse-grained variables combining groups of correlated mutations with the highest collective impact on gene expression. We validate our approach on experimental data for E. coli and discover novel regulatory elements illustrating its deployment at scale across growth conditions.

genomics↗

bioRxiv2026

Species-specific small models for cell type classification approach the performance of large single cell foundation models

Accurate cross-species cell type classification remains a key evaluation task in single-cell transcriptomics. Recent foundation models trained on millions of single-cell profiles demonstrate great in-distribution and out-of-distribution performance on this task, but their large parameter counts and substantial computational costs limit accessibility and interpretability. Here, we introduce CytoType, a simple and interpretable model for cell type classification that leverages pre-trained ESM-2 protein embeddings of protein-coding transcripts. By learning linear, cell-type-specific weights over transcript embeddings, without relying on gene count information, CytoType achieves F1 scores comparable to or exceeding those of large-scale transformer-based models. We further developed ESM-Cell Embedding (ESM-CE), an even simpler variant that only averages ESM-2 embeddings across expressed genes, which also performs competitively against foundation models. Both CytoType and ESM-CE are trained on species-specific data, maintaining high accuracy when classifying cell types with orders of magnitude fewer parameters compared to larger foundation models. For example, for human tissues, the average performance gap between CytoType and the best foundation model was 0.053 F1 points while CytoType uses 10,000x fewer trainable parameters. Additionally, we quantified the contribution of ESM-2 embeddings to cell type classification tasks and demonstrated a three fold reduction in the performance gap between CytoType and the best foundation model for nine species. Finally, we show that CytoTypes learned gene weights are biologically interpretable.

cell biology↗

bioRxiv2025

The Environment-Dependent Regulatory Landscape of the E. coli Genome

1All cells respond to changes in both their internal milieu and the environment around them through the regulation of their genes. Despite decades of effort, there remain huge gaps in our knowledge of both the function of many genes (the so-called y-ome) and how they adapt to changing environments via regulation. Here we describe a joint experimental and theoretical dissection of the regulation of a broad array of more than 100 biologically interesting genes in E. coli across 39 diverse environments, enabling us to identify the binding sites and transcription factors that mediate regulatory control. Using a combination of mutagenesis, massively parallel reporter assays, mass spectrometry, and tools from information theory and statistical physics, we go from complete ignorance of a promoters environment-dependent regulatory architecture to a quantitative description of its binding sites, candidate transcription factors that bind them where identifiable, and the conditions under which they act. As proof of principle of the biological insights to be gained from such a study, we chose a combination of genes from the y-ome, toxin-antitoxin pairs, and genes hypothesized to be part of regulatory modules; we discovered a host of new insights into their underlying regulatory landscape and resulting biological function. We highlight discoveries for y-ome genes, including transcription start sites and transcription factor binding sites at base-pair resolution, and their dependence on growth conditions.

molecular biology↗

bioRxiv2024

Machine learning models of human tissue microbiomes for tissue-of-origin prediction

There is increasing interest in using microbial data diagnostically for tissue health monitoring such as in early cancer detection. To build such models, we need to understand whether normal tissue microbiomes can also be predictive of tissue of origin, and importantly ask how contaminants may contribute to model performance. In this study, using the Tabula Sapiens Microbiome dataset, we built machine learning models of human tissue microbiomes that can predict tissue of origin. This may in part explain how tumor types can be predicted based on the tumor microbiomes. We also demonstrate that machine learning models built using contaminants alone, though not as powerful as those built on true signal, can still predict tissue of origin. Reassuringly, the addition of contaminants to true signal does not increase the performance over models built on true signal. Overall, our findings raise the burden of proof for predictive models of the human tissue and tumor microbiomes. Without addressing the magnitude of contribution from contaminants to model performance, a models reproducibility and its clinical value becomes questionable. We also discuss the optimal microbial taxonomic resolution for building these models.

bioinformatics↗

bioRxiv2022

Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues

The human microbiome has been studied extensively across those sites in the body that are readily accessible to sampling. Internal organs and tissues, however, have remained largely unexplored and, in the absence of infectious disease, are widely assumed to be free of microorganisms. Using single-cell transcriptomic data from the Tabula Sapiens spanning 11 human organ donors, 19 tissue types, 400,000+ annotated cells, 100+ cell types, and [~]70 billion sequences, we created an atlas of the human tissue microbiome with cell type resolution across three domains of life which we refer to as the Tabula Sapiens Microbiome. Together with data from 8 additional donors derived from a different set of experimental techniques for validation, we identified sequences from numerous bacterial, viral and fungal species in human tissues. We mapped the likely microbial flow routes in the body from external-facing microbiomes and natural environments to internal tissues and tumors, demonstrating the existence of several unexpected routes. In the case of tumor microbiomes, we find a considerable number of bacterial genera found in tumors are detectable across tumor-free tissues, suggesting that tumor microbiomes are in part sourced from adjacent normal tissues, even those from distant sites. Finally, by increasing the resolution of sampling from tissues to cell types, we quantified the microbial load and diversity across different human cell types to reveal a network of host cell type and microbe associations. For example, we identified traces of both latent and active Epstein Barr Virus infections in various cell types such as splenic plasma cells. Broad exploration of the human tissue microbiomes may provide insights which ultimately are of clinical importance.

microbiology↗

bioRxiv2022

Fate Bias and Transcriptional Memory of human B cells

Lineage tracking offers a direct approach to study cell fate determination. In this work we combined single cell transcriptomics and lineage tracing to better understand fate-choice in human B cells. Using the antibody sequence to trace cell lineage during in vitro differentiation, we identified intrinsic proliferative and cell fate biases of B cell subtypes. Clonal analysis revealed that IgM memory B cells were more proliferative than any other B cell subtype, and that cells from the same clone had highly concordant fates. We found that transcriptional memory within clones varies across genes, with strongest persistence in genes related to cell fate determination. Similar persistent transcriptional programs were observed in human plasma cells from bone marrow, suggesting that these programs maintain long-term cell fate in vivo. These results show that cell-intrinsic fate bias influences human B cell differentiation and reveal molecular programs underpinning cell fate determination in B cells.

immunology↗

bioRxiv2021

Fly Cell Atlas: a single-cell transcriptomic atlas of the adult fruit fly

The ability to obtain single cell transcriptomes for stable cell types and dynamic cell states is ushering in a new era for biology. We created the Tabula Drosophilae, a single cell atlas of the adult fruit fly which includes 580k cells from 15 individually dissected sexed tissues as well as the entire head and body. Over 100 researchers from the fly community contributed annotations to >250 distinct cell types across all tissues. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types that are shared between tissues, such as blood and muscle cells, allowed the discovery of rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the entire Drosophila community and serves as a comprehensive reference to study genetic perturbations and disease models at single cell resolution.

genomics↗

bioRxiv2020

Quantifying the interplay between rapid bacterial evolution within the mouse intestine and transmission between hosts

Due to limitations on high-resolution strain tracking, selection dynamics during gut-microbiota colonization and transmission between hosts remain mostly mysterious. Here, we introduced hundreds of barcoded Escherichia coli strains into germ-free mice and quantified strain-level dynamics and metagenomic changes. Mutants involved in motility and utilization of abundant metabolites were reproducibly selected within days. Even with rapid selection, coprophagy enforced similar barcode distributions across co-housed mice. Whole-genome sequencing of hundreds of isolates quantified evolutionary dynamics and revealed linked alleles. A population-genetics model predicted substantial fitness advantages for certain mutants and that migration accounted for ~10% of the resident microbiota each day. Treatment with ciprofloxacin demonstrated the interplay between selection and transmission. While initial colonization was mostly uniform, in two mice a bottleneck reduced diversity and selected for ciprofloxacin resistance in the absence of drug. These findings highlight the interplay between environmental transmission and rapid, deterministic selection during evolution of the intestinal microbiota.

microbiology↗

bioRxiv2020

Temporal evolution of single-cell transcriptomes of Drosophila olfactory projection neurons

Neurons undergo substantial morphological and functional changes during development to form precise synaptic connections and acquire specific physiological features. What are the underlying transcriptomic bases? Here, we obtained the single-cell transcriptomes of Drosophila olfactory projection neurons (PNs) at four developmental stages. We decoded the identity of 21 transcriptomic clusters corresponding to 20 PN types and developed methods to match transcriptomic clusters representing the same PN type across development. We discovered that PN transcriptomes reflect unique biological processes unfolding at each stage--neurite growth and pruning during metamorphosis at an early pupal stage; peaked transcriptomic diversity during olfactory circuit assembly at mid-pupal stages; and neuronal signaling in adults. At early developmental stages, PN types with adjacent birth order share similar transcriptomes. Together, our work reveals principles of cellular diversity during brain development and provides a resource for future studies of neural development in PNs and other neuronal types.

neuroscience↗

bioRxiv2020

Tuning TPO-R signaling to influence hematopoietic stem cell differentiation and inhibit essential thrombocythemia

Thrombopoietin (TPO) and the TPO-receptor (TPO-R, or c-MPL)) are essential for hematopoietic stem cell (HSC) maintenance and megakaryocyte differentiation. Agents that can modulate TPO-R signaling are highly desirable, both experimentally and clinically. We have developed a series of surrogate protein-ligands for TPO-R, in the form of diabodies, that homodimerize the TPO-R on the cell surface in different geometries, in effect tuning downstream signaling responses. These surrogate ligands exhibit diverse pharmacological properties, inducing graded signaling outputs, from full to partial TPO agonism and antagonism, thus decoupling the dual functions of TPO/TPO-R. Using scRNA sequencing and HSC self-renewal assays we find that partial agonistic diabodies preserved the stem-like properties of cultured HSCs, but also blocked oncogenic colony formation in Essential Thrombocythemia (ET) through inverse agonism. Our data suggest that dampening downstream TPO signaling is a powerful approach not only for HSC preservation in culture, but also for inhibiting oncogenic signaling through the TPO-R. Significance StatementThe TPO cytokine, which signals through its receptor c-MPL (or TPO-R), is essential for megakaryocyte differentiation and maintenance of hematopoietic stem cells (HSCs). Its signaling is deregulated in Essential Thrombocythemia (ET). Here, we engineered diabodies (DBs) against the TPO-R as surrogate TPO ligands to manipulate TPO-R signaling, from full to partial to antagonism, thus decoupling the dual functions of TPO/TPO-R (i.e, HSC maintenance versus megakaryopoiesis). We subsequently discovered that partial agonistic DBs, by reducing the strength of the TPO-R signal, not only preserved HSCs in culture, but also blocked oncogenic signaling in ET. This finding has the potential to improve HSC cultures for transplants, as well as serve as a unique therapeutic approach for ET.

cell biology↗

bioRxiv2020

Single-cell RNA Expression of SARS-CoV-2 Cell Entry Factors in Human Endometrium during Preconception

We investigated potential SARS-CoV-2 tropism in human endometrium by single-cell RNA-sequencing of viral entry-associated genes in healthy women. Percentages of endometrial cells expressing ACE2, TMPRSS2, CTSB, or CTSL were <2%, 12%, 80%, and 80%, respectively, with 0.7% of cells expressing all four genes. Our findings imply low efficiency of SARS-CoV-2 infection in the endometrium before embryo implantation, providing information to assess preconception risk in asymptomatic carriers.

cell biology↗

bioRxiv2020

Single cell profiling of total RNA using Smart-seq-total

The ability to interrogate total RNA content of single cells would enable better mapping of the transcriptional logic behind emerging cell types and states. However, current RNA-seq methods are unable to simultaneously monitor both short and long, poly(A)+ and poly(A)-transcripts at the single-cell level, and thus deliver only a partial snapshot of the cellular RNAome. Here, we describe Smart-seq-total, a method capable of assaying a broad spectrum of coding and non-coding RNA from a single cell. Built upon the template-switch mechanism, Smart-seq-total bears the key feature of its predecessor, Smart-seq2, namely, the ability to capture full-length transcripts with high yield and quality. It also outperforms current poly(A)-independent total RNA-seq protocols by capturing transcripts of a broad size range, thus, allowing us to simultaneously analyze protein-coding, long non-coding, microRNA and other non-coding RNA transcripts from single cells. We used Smart-seq-total to analyze the total RNAome of human primary fibroblasts, HEK293T and MCF7 cells as well as that of induced murine embryonic stem cells differentiated into embryoid bodies. We show that simultaneous measurement of non-coding RNA and mRNA from the same cell enables elucidation of new roles of non-coding RNA throughout essential processes such as cell cycle or lineage commitment. Moreover, we show that cell types can be distinguished based on the abundance of non-coding transcripts alone.

genomics↗

bioRxiv2019

Coordinating Receptor Expression and Wiring Specificity in Olfactory Receptor Neurons

The ultimate function of a neuron is determined by both its physiology and connectivity, but the transcriptional regulatory mechanisms that coordinate these two features are not well understood1-4. The Drosophila Olfactory receptor neurons (ORNs) provide an excellent system to investigate this question. As in mammals5, each Drosophila ORN class is defined by the expression of a single olfactory receptor or a unique combination thereof, which determines their odor responses, and by the single glomerulus to which their axons target, which determines how sensory signals are represented in the brain6-10. In mammals, the coordination of olfactory receptor expression and wiring specificity is accomplished in part by olfactory receptors themselves regulating ORN wiring specificity11-13. However, Drosophila olfactory receptors do not instruct axon targeting6, 14, raising the question as to how receptor expression and wiring specificity are coordinated. Using single-cell RNA-sequencing and genetic analysis, we identified 33 transcriptomic clusters for fly ORNs. We unambiguously mapped 17 to glomerular classes, demonstrating that transcriptomic clusters correspond well with anatomically and physiologically defined ORN classes. We found that each ORN expresses ~150 transcription factors (TFs), and identified a master TF that regulates both olfactory receptor expression and wiring specificity. A second TF plays distinct roles, regulating only receptor expression in one class and only wiring in another. Thus, fly ORNs utilize diverse transcriptional strategies to coordinate physiology and connectivity.

neuroscience↗

bioRxiv2019

Non-invasive characterization of human bone marrow by cell free messenger-RNA reveals response to growth factor stimulation and hematopoietic reconstitution after transplantation

Circulating cell free mRNA (cf-mRNA) holds great promise as a non-invasive diagnostic biomarker. However, the biological origin of cf-mRNA is still not well understood, limiting the clinical applications of this technology. Here, we use the bone marrow (BM) and pharmacologic manipulation of its resident cells as a window to study the origin of cf-mRNA. Using NGS-based profiling, we show that cf-mRNA is enriched in transcripts derived from the BM compared to circulating cells. Further, BM ablation experiments followed by hematopoietic stem cell transplants in cancer patients show that cf-mRNA levels reflect the transcriptional activity of BM resident hematopoietic lineages during marrow reconstitution. Finally, by stimulating specific BM cell populations in vivo using growth factor therapeutics (i.e. EPO, G-CSF), we show that cf-mRNA reveals dynamic functional changes in growing cell types, suggesting that, unlike other cell-free nucleic acids, cf-mRNA is secreted from living cells, rather than exclusively from apoptotic cells. Our results shed new light on the biology of cf-mRNA and demonstrate its potential applications in clinical practice.

molecular biology↗

bioRxiv2018

A mouse tissue atlas of small non-coding RNA

Small non-coding RNAs (ncRNAs) play a vital role in a broad range of biological processes both in health and disease. A comprehensive quantitative reference of small ncRNA expression would significantly advance our understanding of ncRNA roles in shaping tissue functions. Here, we systematically profiled the levels of five ncRNA classes (miRNA, snoRNA, snRNA, scaRNA and tRNA fragments) across eleven mouse tissues by deep sequencing. Using fourteen biological replicates spanning both sexes, we identified that ~ 30% of small ncRNAs are distributed across the body in a tissue-specific manner with some are also being sexually dimorphic. We found that miRNAs are subject to "arm switching" between healthy tissues and that tRNA fragments are retained within tissues in both a gene- and a tissue-specific manner. Out of eleven profiled tissues we confirmed that brain contains the largest number of unique small ncRNA transcripts, some of which were previously annotated while others are identified for the first time in this study. Furthermore, by combining these findings with single-cell ATAC-seq data, we were able to connect identified brain-specific ncRNA with their cell types of origin. These results yield the most comprehensive characterization of specific and ubiquitous small RNAs in individual murine tissues to date, and we expect that this data will be a resource for the further identification of ncRNAs involved in tissue-function in health and dysfunction in disease. HIGHLIGHTS- An atlas of tissue levels of multiple small ncRNA classes generated from 14 biological replicates of both sexes across 11 tissues - Distinct distribution patterns of miRNA arms and tRNA fragments across tissues suggest the existence of tissue-specific mechanisms of ncRNA cleavage and retention - miRNA expression is sex specific in healthy tissues - Small RNA-seq and scATAC-seq data integration produce a detailed map of cell-type specific ncRNA profiles in the mouse brain

genomics↗

bioRxiv2018

Hydrogenotrophic methanogenesis in archaeal phylum Verstraetearchaeota reveals the shared ancestry of all methanogens

Methanogenic archaea are major contributors to the global carbon cycle and were long thought to belong exclusively to the euryarchaeotal phylum. Discovery of the methanogenesis gene cluster methyl-coenzyme M reductase (Mcr) in the Bathyarchaeota and thereafter the Verstraetearchaeota led to a paradigm shift, pushing back the evolutionary origin of methanogenesis to pre-date that of the Euryarchaeota. The methylotrophic methanogenesis found in the non-Euryarchaota distinguished itself from the predominantly hydrogenotrophic methanogens found in euryarchaeal orders as the former do not couple methanogenesis to carbon fixation through the reductive acetyl-coenzyme A (Wood-Ljungdahl) pathway, which was interpreted as evidence for independent evolution of the two methanogenesis pathways. Here, we report the discovery of a complete and divergent hydrogenotrophic methanogenesis pathway in a novel, thermophilic order of the Verstraetearchaeota which we have named Candidatus Methanohydrogenales, as well as the presence of the Wood-Ljungdahl pathway in the crenarchaeal order Desulfurococcales. Our findings support the ancient origin of hydrogenotrophic methanogenesis, suggest that methylotrophic methanogenesis might be a later adaptation of specific orders, and provide insight into how transition from hydrogenotrophic to methylotrophic methanogenesis might occur.

microbiology↗