Biology subjects

Teyssier, N.

Publications and source records attributed to Teyssier, N..

13 recordsLinked to original sources

bioRxiv2026

Scalable probe-based single-cell transcriptional profiling for virtual cell perturbation mapping and synthetic biology phenotyping

Large-scale single-cell transcriptional phenotyping of genetic perturbations (perturb-seq) links genes to phenotypes and should enable virtual cell predictive modeling and cellular engineering. However, current perturb-seq single-cell methods are costly, information sparse and require barcodes for many applications. We developed ProPer-seq, a perturb-seq method that uses multiplexed custom DNA probe panels to measure and phenotype synthetic biology perturbations at single-cell resolution without barcodes, including multidomain proteins and sgRNAs. ProPer-seq faithfully reproduces gold-standard perturb-seq phenotypes while achieving 4-fold cost reduction and 50% increased gene detection per cell. As a scalable fixed-cell profiling method, ProPer-seq enables atlas-scale profiling for virtual-cell initiatives and demonstrates data quality suitable for training and validating predictive models. Lastly, ProPer-seqs targeted detection of modular transgenes enables library-on-library perturbation profiling of combinatorial synthetic protein design spaces. We applied this to 3,550 sgRNA x dCas9 effector combinations as well as 260 CAR x ORF combinations dynamically profiled in primary T cells, revealing principles of transcriptional control and cell state modulation by multidomain synthetic transgenes.

synthetic biology↗

bioRxiv2026

cyto: ultra high-throughput processing of 10x-flex single cell sequencing

Single-cell genomics is rapidly scaling toward billion-cell atlases, but computational analysis has become a critical bottleneck. Processing multiplexed datasets with existing tools requires substantial computational resources and runtime that become prohibitive at scale. Here we present cyto, an ultra highthroughput processor for 10x Genomics Flex single-cell sequencing optimized for production-scale analysis. cyto exploits the fixed sequence geometry of Flex libraries through direct k-mer lookup rather than alignment-based mapping, and introduces IBU (Indexed-Barcode-UMI), a compact binary format for efficient read processing. cyto further leverages BINSEQ, a binary sequencing format that enables highly parallel parsing and overcomes the single-threaded limitations of gzip compression. On a benchmark 320,000-cell multiplexed dataset, cyto completes processing in 13 minutes compared to CellRangers 3.7 hours, a 16.5-fold speedup, while requiring 2.4-fold less memory and performing 5.6-fold less disk I/O. The 31.7-fold reduction in CPU-hours represents true algorithmic efficiency rather than parallelization alone. Critically, cyto maintains 99.85% concordance with CellRanger outputs, with identical cell type clustering in dimensionality reduction analyses. These performance improvements enable costeffective processing on smaller cloud instances and make previously prohibitive experiments computationally feasible. cyto is production-ready, open-source software that provides the computational foundation for atlas-scale single-cell projects and genome-wide perturbation screens.

bioinformatics↗

bioRxiv2025

A genetic screen in enteroendocrine cells reveals mechanisms that control protein sensing and GLP-1 release

Enteroendocrine cells (EECs) are the principal nutrient sensors in the gastrointestinal (GI) tract and release hormones such as glucagon-like-peptide 1 (GLP-1) that modulate GI function and appetite. While some of the molecules involved in nutrient sensing within EECs have been described, there have been no systematic studies to map the relevant genes and pathways. Here, we developed a strategy to perform a high-throughput screen for genes that are required for nutrient-induced activation of EECs, and we applied this to probe mechanisms for sensing dietary protein. We found that all of the genes previously proposed to function as protein sensors in EECs are, collectively, dispensable for protein sensing in an EEC cell line. Instead, a screen of >20,000 sgRNAs identified numerous genes associated with mitochondrial respiration as being necessary for this process. We showed through secondary assays that impairing oxidative phosphorylation (OXPHOS) reduced EEC activation and GLP-1 release in response to nutrients but not in response to a non-nutritive stimulus. On the other hand, boosting OXPHOS increased EEC activation and GLP-1 release. These data reveal that intracellular metabolism within EECs controls the detection of dietary protein, possibly by monitoring the entry of ingested amino acids into the TCA cycle. More broadly, these findings suggest a general strategy to screen for genes and pathways that might be used to boost the nutrient-regulated release of gut peptides such as GLP-1.

physiology↗

bioRxiv2025

Predicting cellular responses to perturbation across diverse contexts with STATE

Cellular responses to perturbations are a cornerstone for understanding biological mechanisms and selecting drug targets. While machine learning models offer tremendous potential for predicting perturbation effects, they currently struggle to generalize to unobserved cellular contexts. Here, we introduce SO_SCPLOWTATEC_SCPLOW, a transformer model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. SO_SCPLOWTATEC_SCPLOW predicts perturbation effects across sets of cells and is trained using gene expression data from over 100 million perturbed cells. SO_SCPLOWTATEC_SCPLOW improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling and chemical perturbations with significantly improved accuracy. Using its cell embedding trained on observational data from 167 million cells, SO_SCPLOWTATEC_SCPLOW identified strong perturbations in novel cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that highlights SO_SCPLOWTATEC_SCPLOWs ability to detect cell type-specific perturbation responses, such as cell survival. Overall, the performance and flexibility of SO_SCPLOWTATEC_SCPLOW sets the stage for scaling the development of virtual cell models.

systems biology↗

bioRxiv2025

Regulators of Interferon-Responsive Microglia Uncovered by Genome-wide CRISPRi Screening

Microglia dynamically support brain homeostasis through the induction of specialized activation programs or states. One such program is the Interferon-Responsive Microglia state (IRM), which has been identified in developmental windows, aging, and disease. While the functional importance of this state is becoming increasingly clear, our understanding of the regulatory networks that govern IRM induction remain incomplete. To systematically identify genetic regulators of the IRM state, we conducted a genome-wide CRISPR interference (CRISPRi) screen in human iPSC-derived microglia (iPS-Microglia) using IFIT1 as a representative IRM marker. We identified 772 genes that modulate IRM, including canonical type I interferon signaling genes (IFNAR2, TYK2, STAT1/2, USP18) and novel regulators. We uncovered a non-canonical role for the CCR4-NOT complex subunit CNOT10 in IRM activation, independent of its traditional function. This work provides a comprehensive resource for dissecting IRM biology and highlights both established and novel targets for modulating microglial interferon signaling in health and disease.

neuroscience↗

bioRxiv2025

BINSEQ: A Family of High-Performance Binary Formats for Nucleotide Sequences

AO_SCPLOWBSTRACTC_SCPLOWModern genomics produces billions of sequencing records per run, which are typically stored as gzip-compressed FASTQ files. While this format is widely used, it is not optimal for high-throughput processing due to its reliance on single-threaded decompression and sequential parsing of irregularly sized records. This limitation is particularly problematic for applications that would benefit from parallel processing, such as read mapping, variant calling, and de novo assembly. Here, we present BINSEQ, a family of simple binary formats that enable high-throughput parallel processing of sequencing data. The BINSEQ family consists of two complementary implementations: BQ, optimized for fixed-length reads using a two-bit or four-bit encoding scheme with true random record access capability, and VBQ, designed for variable-length sequences with optional quality scores and block-based compression. We demonstrate that BINSEQ files are up to 90x faster than compressed FASTQ for parallel processing and can reduce analysis time from hours to minutes for large-scale genome and transcriptome analyses, particularly for resource-intensive applications like alignment, mapping, and de novo assembly. To facilitate adoption we provide high-performance libraries for reading and writing BINSEQ formats, native parallelization strategies with convenient APIs, and a command-line tool for conversion to and from traditional formats. Author SummaryModern sequencing technologies routinely generate billions of reads per experiment, yet the methods for storing and accessing this data have not kept pace. Sequencing reads remain predominantly stored in FASTQ, a text-based format designed for far smaller datasets. FASTQs sequential parsing requirements and practical need for compression create a fundamental mismatch with modern multi-core architectures, where data access rather than computation has become the primary bottleneck. We address this problem with BINSEQ, a family of binary formats engineered for random access and native parallelization. Systematic benchmarking across applications of varying computational complexity demonstrates that BINSEQ achieves 90-fold improvements in data access and maintains substantial advantages in compute-intensive tasks such as genome alignment, reducing runtimes from hours to minutes. We present two complementary implementations: BQ, optimized for simplicity and maximal throughput, and VBQ, designed for flexibility while maintaining high performance. By reconsidering the relationship between storage architecture and parallel processing capabilities, BINSEQ provides a practical solution to a critical infrastructure challenge in high-throughput genomics.

bioinformatics↗

bioRxiv2025

scBaseCamp: An AI agent-curated, uniformly processed, and continually expanding single cell data repository

Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata and the cost of processing reads. Here, we introduce scBaseCount, a single-cell RNA sequencing database that leverages an AI agent to automate discovery and metadata extraction, and standardize data processing. Built by directly mining all 10x Genomics datasets from SRA, scBaseCount is the largest freely accessible public repository of single-cell gene expression data, comprising over 502 million cells across 27 organisms and 75 tissues, offering an unbiased view of the composition of data within SRA. Uniform processing enables measurement of both intronic and exonic reads, non-coding gene expression and improves alignment across experiments as well as the performance of AI models trained on this phenotypically diverse data. Moreover, scBaseCount provides a blueprint for how AI can be leveraged to curate and autonomously update large biological data repositories.

bioinformatics↗

bioRxiv2024

A Massively Parallel CRISPR-Based Screening Platform for Modifiers of Neuronal Activity

Understanding the complex interplay between gene expression and neuronal activity is crucial for unraveling the molecular mechanisms underlying cognitive function and neurological disorders. Here, we developed pooled screens for neuronal activity, using CRISPR interference (CRISPRi) and the fluorescent calcium integrator CaMPARI2. Using this screening method, we evaluated 1343 genes for their effect on excitability in human iPSC-derived neurons, revealing potential links to neurodegenerative and neurodevelopmental disorders. These genes include known regulators of neuronal excitability, such as TARPs and ion channels, as well as genes associated with autism spectrum disorder and Alzheimers disease not previously described to affect neuronal excitability. This CRISPRi-based screening platform offers a versatile tool to uncover molecular mechanisms controlling neuronal activity in health and disease.

neuroscience↗

bioRxiv2023

Autism genes converge on microtubule biology and RNA-binding proteins during excitatory neurogenesis

Recent studies have identified over one hundred high-confidence (hc) autism spectrum disorder (ASD) genes. Systems biological and functional analyses on smaller subsets of these genes have consistently implicated excitatory neurogenesis. However, the extent to which the broader set of hcASD genes are involved in this process has not been explored systematically nor have the biological pathways underlying this convergence been identified. Here, we leveraged CROP-Seq to repress 87 hcASD genes in a human in vitro model of cortical neurogenesis. We identified 17 hcASD genes whose repression significantly alters developmental trajectory and results in a common cellular state characterized by disruptions in proliferation, differentiation, cell cycle, microtubule biology, and RNA-binding proteins (RBPs). We also characterized over 3,000 differentially expressed genes, 286 of which had expression profiles correlated with changes in developmental trajectory. Overall, we uncovered transcriptional disruptions downstream of hcASD gene perturbations, correlated these disruptions with distinct differentiation phenotypes, and reinforced neurogenesis, microtubule biology, and RBPs as convergent points of disruption in ASD.

systems biology↗

bioRxiv2023

GIA: A genome interval arithmetic toolkit for high performance interval set operations

MotivationThis study addresses the pressing need for efficient interval techniques in processing vast genomic datasets, such as those generated by ChIP-seq, RNA-seq, and whole-genome sequencing. Intervals are critical in characterizing biological features, necessitating streamlined interval manipulation for meaningful insights. Existing tools often struggle with memory and runtime requirements when managing extensive genomic region arithmetic. ResultsThe study introduces GIA (Genomic Interval Arithmetic) and BEDRS, a novel command-line tool and a rust library that significantly enhance the performance of genomic interval analysis. GIA outperforms existing tools like BEDOPS, BEDTools, and GenomicRanges by a factor of 2x to 20x across a range of operations. These advances enable researchers to perform genomic interval operations more efficiently, drastically reducing computational time and resource requirements in high-throughput genomic sequencing analysis. Availability and Implementation- https://github.com/noamteyssier/gia - https://github.com/noamteyssier/bedrs

bioinformatics↗

bioRxiv2023

Higher-order combinatorial chromatin perturbations by engineered CRISPR-Cas12a for functional genomics

Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting 1-3 genomic sites per cell. To develop a tool for higher-order (>3) combinatorial targeting of genomic sites with CRISPRi in functional genomics screens, we engineered an Acidaminococcus Cas12a variant -- referred to as multiplexed transcriptional interference AsCas12a (multiAsCas12a). multiAsCas12a incorporates a key mutation, R1226A, motivated by the hypothesis of nicking-induced stabilization of the ribonucleoprotein:DNA complex for improving CRISPRi activity. multiAsCas12a significantly outperforms prior state-of-the-art Cas12a variants in combinatorial CRISPRi targeting using high-order multiplexed arrays of lentivirally transduced CRISPR RNAs (crRNA), including in high-throughput pooled screens using 6-plex crRNA array libraries. Using multiAsCas12a CRISPRi, we discover new enhancer elements and dissect the combinatorial function of cis-regulatory elements. These results instantiate a group testing framework for efficiently surveying potentially numerous combinations of chromatin perturbations for biological discovery and engineering.

genetics↗

bioRxiv2022

Proteome-wide antigenic profiling in Ugandan cohorts identifies associations between age, exposure intensity, and responses to repeat-containing antigens in Plasmodium falciparum

Protection against Plasmodium falciparum, which is primarily antibody-mediated, requires recurrent exposure to develop. The study of both naturally acquired limited immunity and vaccine induced protection against malaria remains critical for ongoing eradication efforts. Towards this goal, we deployed a customized P. falciparum PhIP-seq T7 phage display library containing 238,068 tiled 62-amino acid peptides, covering all known coding regions, including antigenic variants, to systematically profile antibody targets in 198 Ugandan children and adults from high and moderate transmission settings. Repeat elements - short amino acid sequences repeated within a protein - were significantly enriched in antibody targets. While breadth of responses to repeat-containing peptides was twofold higher in children living in the high versus moderate exposure setting, no such differences were observed for peptides without repeats, suggesting that antibody responses to repeat-containing regions may be more exposure dependent and/or less durable in children than responses to regions without repeats. Additionally, short motifs associated with seroreactivity were extensively shared among hundreds of antigens, potentially representing cross- reactive epitopes. PfEMP1 shared motifs with the greatest number of other antigens, partly driven by the diversity of PfEMP1 sequences. These data suggest that the large number of repeat elements and potential cross-reactive epitopes found within antigenic regions of P. falciparum could contribute to the inefficient nature of malaria immunity.

immunology↗

bioRxiv2018

FLASH: A next-generation CRISPR diagnostic for multiplexed detection of antimicrobial resistance sequences

The growing prevalence of deadly microbes with resistance to previously life-saving drug therapies is a dire threat to human health. Detection of low abundance pathogen sequences remains a challenge for metagenomic Next Generation Sequencing (NGS). We introduce FLASH (Finding Low Abundance Sequences by Hybridization), a next-generation CRISPR/Cas9 diagnostic method that takes advantage of the efficiency, specificity and flexibility of Cas9 to enrich for a programmed set of sequences. FLASH-NGS achieves up to 5 orders of magnitude of enrichment and sub-attomolar gene detection with minimal background. We provide an open-source software tool (FLASHit) for guide RNA design. Here we applied it to detection of antimicrobial resistance genes in respiratory fluid and dried blood spots, but FLASH-NGS is applicable to all areas that rely on multiplex PCR.

microbiology↗