Biology subjects

Schiebinger, G.

Publications and source records attributed to Schiebinger, G..

13 recordsLinked to original sources

bioRxiv2026

DemuxHMM: Large-Scale Single-Cell Embryo Profiling via Recombination Barcoding

High-resolution developmental time-courses with single-cell RNA sequencing (scRNA-seq) increasingly target trajectory inference and other analyses in the study of development and disease [1-6]. These datasets are often generated by pooling individuals and inferring cell-to-individual mappings after sequencing, in a process called demultiplexing. Existing demultiplexing methods are limited in the number of timepoints they can support, due to either the need for individual-by-individual processing or reduced accuracy at large numbers of individuals. To address these limitations, we introduce a combined experimental and computational framework for creating large-scale, individual-resolved datasets. Our framework couples a simple breeding scheme that creates contiguous SNP patterns (recombination barcodes) with a recombination-aware demultiplexing method, DemuxHMM, that explicitly models this structure with a Hidden Markov Model (HMM). We demonstrate substantial performance and scalability gains from this combined approach on simulated data, highlighting its potential to enable the creation of large-scale single-cell time series.

bioinformatics↗

bioRxiv2025

Global StationaryOT: Trajectory inference for aging time courses of single-cell snapshots

Trajectory inference (TI) methods for single-cell snapshots of developmental systems have yielded numerous insights into the gene regulatory networks (GRNs) that control cell differentiation. Many TI algorithms have been proposed for recovering cell trajectories from single samples containing cells spanning a spectrum of differentiation states; however, these methods cannot leverage temporal information when a time course of such diverse samples is available. As interest grows in understanding how the regulation of GRNs changes as an organism ages, current TI theory and methods must be adapted to take advantage of all information in aging time courses of single-cell data. In this paper, we present our novel age-conscious method, global StationaryOT, which exploits the temporal information in aging time courses to simultaneously reconstruct debiased cell trajectories at all ages. We demonstrate that this first-of-its-kind method achieves more accurate, biologically consistent trajectories in synthetic and real biological contexts where data sparsity produces significant noise in the outputs of current TI methods when they are applied to time course samples independently.

bioinformatics↗

bioRxiv2024

Single-cell transcriptomics reveals evolutionary reconfiguration of embryonic cell fate specification in the sea urchin Heliocidaris erythrogramma

Altered regulatory interactions during development likely underlie a large fraction of phenotypic diversity within and between species, yet identifying specific evolutionary changes remains challenging. Analysis of single-cell developmental transcriptomes from multiple species provides a powerful framework for unbiased identification of evolutionary changes in developmental mechanisms. Here, we leverage a "natural experiment" in developmental evolution in sea urchins, where a major life history switch recently evolved in the lineage leading to Heliocidaris erythrogramma, precipitating extensive changes in early development. Comparative analyses of scRNA-seq developmental time courses from H. erythrogramma and Lytechinus variegatus (representing the derived and ancestral states respectively) reveals numerous evolutionary changes in embryonic patterning. The earliest cell fate specification events, and the primary signaling center are co-localized in the ancestral dGRN but remarkably, in H. erythrogramma they are spatially and temporally separate. Fate specification and differentiation are delayed in most embryonic cell lineages, although in some cases, these processes are conserved or even accelerated. Comparative analysis of regulator-target gene co-expression is consistent with many specific interactions being preserved but delayed in H. erythrogramma, while some otherwise widely conserved interactions have likely been lost. Finally, specific patterning events are directly correlated with evolutionary changes in larval morphology, suggesting that they are directly tied to the life history shift. Together, these findings demonstrate that comparative scRNA-seq developmental time courses can reveal a diverse set of evolutionary changes in embryonic patterning and provide an efficient way to identify likely candidate regulatory interactions for subsequent experimental validation.

evolutionary biology↗

bioRxiv2023

Time- and lineage-resolved transcriptional profiling uncovers gene expression programs and clonal relationships that underlie human T lineage specification

T cells develop from multi-potent hematopoietic progenitors in the thymus and provide adaptive protection against pathogens and cancer. However, the emergence of human T cell-competent blood progenitors, and their subsequent specification to the T lineage, has been challenging to capture in real time. Here, we leveraged a pluripotent stem cell differentiation system to understand the transcriptional dynamics and cell fate restriction events that underlie this critical developmental process. Time-resolved single cell RNA sequencing revealed that cell-cycle exit, downregulation of the multipotent hematopoietic program, and upregulation of >90 lineage-associated transcription factors all occur within a highly co-ordinated and narrow developmental window. Computational gene-regulatory network inference elucidated the transcriptional logic of T lineage specification, uncovering an important role for YBX1. We mapped the differentiation cell fate hierarchy using transcribed lineage barcoding and mathematical trajectory inference and discovered that mast and myeloid potential bifurcate from each other early in haematopoiesis, upstream of T lineage restriction. Collectively, our analyses provide a quantitative, time-resolved model of human T cell specification with relevance for regenerative medicine and developmental immunology.

immunology↗

bioRxiv2023

Reconstruction of single cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and intricate mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport (OT) analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4, LAMA3, and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF, CKS1B, and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the latter group of genes represents newly identified regulators, shedding light on potential targets for modulating EMT trajectories. Significance StatementIn our study, we investigated cellular trajectories during EMT using a time-series scRNAseq dataset. OT analysis was used to infer cell-to-cell connections from scRNAseq data, allowing us to predict cell linkages and overcome limitations of sequencing such as the need to sacrifice cells for each measurement. This approach allowed us to identify diverse EMT responses under uniform treatment, a significant advancement over previous studies limited by the static nature of scRNAseq data. Our analysis identified a broad set of genes involved in the EMT process, uncovering novel insights such as the upregulation of cell cycle genes in cells predisposed to a high EMT state and the enhancement of cell adhesion marker genes in cells veering towards a partial EMT state. This work enriches our understanding of the dynamic processes of EMT, showcasing the varied cellular fates within the same experimental setup.

systems biology↗

bioRxiv2023

Cellular proliferation biases clonal lineage tracing and trajectory inference

We identify a fundamental statistical phenomenon in single-cell time courses with clone-based lineage tracing. Through simple probabilistic arguments, we show how the relative growth rates of cells influence the probability that they will be sampled in clones observed across multiple time points. We support these arguments with a simple simulation study and a time-course of T-cell development, and we demonstrate that this bias can impact fate probability predictions from trajectory inference methods. Finally, we explore how to develop trajectory inference methods which are robust to this bias. In particular, we show how to extend LineageOT [1] to use data from clones observed across multiple time points.

bioinformatics↗

bioRxiv2022

Brassinosteroid gene regulatory networks at cellular resolution

Brassinosteroids (BRs) are plant steroid hormones that regulate diverse processes such as cell division and cell elongation. BRs control thousands of genes through gene regulatory networks that vary in space and time. By using time-series single-cell RNA-sequencing to identify BR-responsive gene expression specific to different cell types and developmental stages of the Arabidopsis root, we uncovered the elongating cortex as a site where BRs trigger a shift from proliferation to elongation associated with increased expression of cell wall-related genes. Our analysis revealed HAT7 and GTL1 as BR-responsive transcription factors that regulate cortex cell elongation. These results establish the cortex as an important site for BR-mediated growth and unveil a BR signaling network regulating the transition from proliferation to elongation, illuminating new aspects of spatiotemporal hormone response.

plant biology↗

bioRxiv2022

The DNA-based global positioning system--a theoretical framework for large-scale spatial genomics

We present GPS-seq, a theoretical framework that enables massively scalable, optics-free spatial transcriptomics. GPS-seq combines data from high-throughput sequencing with manifold learning to obtain the spatial transcriptomic landscape of a given tissue section without optical microscopy. In this framework, similar to technologies like Slide-seq and 10X Visium, tissue samples are stamped on a surface of randomly-distributed DNA-barcoded spots (or beads). The transcriptomic sequences of proximal cells are fused to DNA barcodes, enabling the recovery of a transcriptomic pixel image by high-throughput sequencing. The barcode spots serve as "anchors" which also capture spatially diffused "satellite" barcodes, and therefore allow computational reconstruction of spot positions without optical sequencing or depositing barcodes to pre-specified positions. In theory, it could generate 100 mm x 100 mm spatial transcriptomic images with 10-20 m resolution by localizing 108 DNA-barcoded pixels with a single Illumina NovaSeq run. The general framework of GPS-seq is also compatible with standard single-cell (or single-nucleus) capture methods, and any modality of single-cell genomics, such as sci-ATAC-seq, could be transformed into spatial genomics in this strategy. We envision that GPS-seq will lead to breakthrough discoveries in diverse areas of biology by enabling organ-scale imaging of multiple genomic statuses at single-cell resolution for the first time.

bioinformatics↗

bioRxiv2021

Hematopoiesis at single cell resolution spanning human development and maturation

Hematopoiesis is a process of constitutive regeneration whereby hematopoietic stem and progenitor cells (HSPCs) replenish mature blood cells. During maturation and aging, HSPCs shift their output to support the demands of prenatal development and postnatal maturation both at homeostasis and in response to stress. How HSPC ontogeny changes throughout life is unknown; studies to date have largely focused on specific individual ages, particularly at single cell resolution. Here, we performed single cell RNA-seq of human HSPCs from early prenatal development into mature adulthood. We observed shifts in HSPC transcriptional states and differentiation trajectories over time. We identified age-specific gene expression patterns throughout human maturation and developed methods for identifying, prospectively purifying, and functionally validating age-specific HSC states. Together, our findings define the temporal maturation of human HSPCs and uncover principles applicable to age-biased blood diseases. SummarySingle cell RNA sequencing reveals that the mechanisms of human hematopoietic stem and progenitor cell (HSPC) fate commitment change over a lifetime from gestation to mature adulthood.

developmental biology↗

bioRxiv2021

Optimal transport analysis reveals trajectories in steady-state systems

Understanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore optimal transport provides a unified approach to inferring trajectories, applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddingtons epigenetic landscape. We implemented StationaryOT as a software package and demonstrate its efficacy when applied to simulated data as well as single-cell data from Arabidopsis thaliana root development.

bioinformatics↗

bioRxiv2020

Developmental Single-cell transcriptomics in the Lytechinus variegatus 1 Sea Urchin Embryo

Here we employed scRNA-seq coupled with computational approaches to examine molecular changes in cells during specification and differentiation. We examined the first 24 hours of development of the sea urchin Lytechinus variegatus (Lv) with 18 time points during which the embryo develops to the larval stage. Using Waddington-OT, the time points were computationally "stitched" together to calculate developmental trajectories. Skeletogenic cells displayed the expected immediate early divergence while other lineages diverged asynchronously, with many cells retaining an intermediate specification status until late in gastrulation. The Lv-scRNA-seq dataset was compared to the developmental Gene Regulatory Network (dGRN) model of specification in Strongylocentrotus purpuratus (Sp). 79 of 80 genes (98%) in that dGRN are present in the Lv-scRNA-seq dataset, and expressed in the correct lineages in which the dGRN circuits operate. Surprisingly, however, many heterochronies in timing of first expression of dGRN genes have evolved between the two species. Replotting the two dGRNs with precise attention to time of expression revealed a number of feedback inputs that likely buffer the dGRNs, allowing them to maintain function in the face of accumulating heterochronies. Summary statementThe early development of the sea urchin embryo was followed using scRNA-seq plus computational methods to trace lineage diversifications. These were matched to gene regulatory network changes over time.

developmental biology↗

bioRxiv2020

A Unified Framework for Lineage Tracing and Trajectory Inference

Understanding the genetic and epigenetic programs that control differentiation during development is a fundamental challenge, with broad impacts across biology and medicine. New measurement technologies like single-cell RNA-sequencing and CRISPR-based lineage tracing have opened new windows on these processes, through computational trajectory inference and lineage reconstruction. While these two mathematical problems are deeply related, they been approached from separate directions: methods for trajectory inference are not typically designed to leverage information from lineage tracing and vice versa. We present a novel, unified framework for lineage tracing and trajectory inference. Specifically, we develop a method for reconstructing developmental trajectories from time courses with snapshots of both cell states and lineages, leveraging mathematical tools from graphical models and optimal transport. We find that lineage data helps disentangle complex state transitions with increased accuracy using fewer measured time points. Moreover, integrating lineage tracing with trajectory inference in this way enables accurate reconstruction of developmental pathways that are impossible to recover with state-based methods alone.

developmental biology↗

bioRxiv2017

Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming.

Understanding the molecular programs that guide cellular differentiation during development is a major goal of modern biology. Here, we introduce an approach, WADDINGTON-OT, based on the mathematics of optimal transport, for inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories from large-scale single-cell RNA-seq (scRNA-seq) data collected along a time course. We demonstrate the power of WADDINGTON-OT by applying the approach to study 65,781 scRNA-seq profiles collected at 10 time points over 16 days during reprogramming of fibroblasts to iPSCs. We construct a high-resolution map of reprogramming that rediscovers known features; uncovers new alternative cell fates including neuraland placental-like cells; predicts the origin and fate of any cell class; highlights senescent-like cells that may support reprogramming through paracrine signaling; and implicates regulatory models in particular trajectories. Of these findings, we highlight Obox6, which we experimentally show enhances reprogramming efficiency. Our approach provides a general framework for investigating cellular differentiation.

bioinformatics↗