Biology subjects

Klein, A. M.

Publications and source records attributed to Klein, A. M..

6 recordsLinked to original sources

bioRxiv2018

Scrublet: computational identification of cell doublets in single-cell transcriptomic data

Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Scrublet (Single-Cell Remover of Doublets), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets.

bioinformatics↗

bioRxiv2018

Emergence of the erythroid lineage from multipotent hematopoiesis

Red cell formation begins with the hematopoietic stem cell, but the manner by which it gives rise to erythroid progenitors, and their subsequent developmental path, remain unclear. Here we combined single-cell transcriptomics of murine hematopoietic tissues with fate potential assays to infer a continuous yet hierarchical structure for the hematopoietic network. We define the erythroid differentiation trajectory as it emerges from multipotency and diverges from 6 other blood lineages. With the aid of a new flow-cytometric sorting strategy, we validated predicted cell fate potentials at the single cell level, revealing a coupling between erythroid and basophil/mast cell fates. We uncovered novel growth factor receptor regulators of the erythroid trajectory, including the proinflammatory IL-17RA, found to be a strong erythroid stimulator; and identified a global hematopoietic response to stress erythropoiesis. We further identified transcriptional and high-purity FACS gates for the complete isolation of all classically-defined erythroid burst-forming (BFU-e) and colony-forming progenitors (CFU-e), finding that they express a dedicated transcriptional program, distinct from that of terminally-differentiating erythroblasts. Intriguingly, profound remodeling of the cell cycle is intimately entwined with CFU-e developmental progression and with a sharp transcriptional switch that extinguishes the CFU-e stage and activates terminal differentiation. Underlying these results, our work showcases the utility of theoretic approaches linking transcriptomic data to predictive fate models, providing key insights into lineage development in vivo.

systems biology↗

bioRxiv2017

Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain by scGESTALT

Hundreds of cell types are generated during development, but their lineage relationships are largely elusive. Here we report a technology, scGESTALT, which combines cell type identification by single-cell RNA sequencing with lineage recording by cumulative barcode editing. We sequenced ~60,000 transcriptomes from the juvenile zebrafish brain and identified more than 100 cell types and marker genes. We engineered an inducible system that combines early and late barcode editing and isolated thousands of single-cell transcriptomes and their associated barcodes. The large diversity of edited barcodes and cell types enabled the generation of lineage trees with hundreds of branches. Inspection of lineage trajectories identified restrictions at the level of cell types and brain regions and helped uncover gene expression cascades during differentiation. These results establish scGESTALT as a new and widely applicable tool to simultaneously characterize the molecular identities and lineage histories of thousands of cells during development and disease.

developmental biology↗

bioRxiv2017

Fundamental limits on dynamic inference from single cell snapshots

Single cell expression profiling reveals the molecular states of individual cells with unprecedented detail. However, because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. But some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, Population Balance Analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements.\n\nSignificanceSeeing a snapshot of individuals at different stages of a process can reveal what the process would look like for a single individual over time. Biologists apply this principle to infer temporal sequences of gene expression states in cells from measurements made at a single moment in time. However, these inferences are fundamentally under-determined. Using a conservation law, we enumerate reasons that there is no unique dynamics associated with a single snapshot, limiting our ability to infer gene regulatory mechanisms. We then propose a method for dynamic inference that provides a unique dynamic solution under defined approximations and apply it to data from bone marrow stem cells. Overall, this study introduces formal biophysical approaches to single cell bioinformatics.\n\nClassificationBIOLOGICAL SCIENCES / Systems Biology

systems biology↗

bioRxiv2017

Epidermal stem cells self-renew upon neighboring differentiation

Many adult tissues are dynamically sustained by the rapid turnover of stem cells. Yet, how cell fates such as self-renewal and differentiation are orchestrated to achieve long-term homeostasis remains elusive. Studies utilizing clonal tracing experiments in multiple tissues have argued that while stem cell fate is balanced at the population level, individual cell fate - to divide or differentiate - is determined intrinsically by each cell seemingly at random ( 1 2 3 4 5). These studies leave open the question of how cell fates are regulated to achieve fate balance across the tissue. Stem cell fate choices could be made autonomously by each cell throughout the tissue or be the result of cell coordination ( 6 7). Here we developed a novel live tracking strategy that allowed recording of every division and differentiation event within a region of epidermis for a week. These measurements reveal that stem cell fates are not autonomous. Rather, direct neighbors undergo coupled opposite fate decisions. We further found a clear ordering of events, with self-renewal triggered by neighbor differentiation, but not vice-versa. Typically, around 1-2 days after cell delamination, a neighboring cell entered S/G2 phase and divided. Functional blocking of this local feedback showed that differentiation continues to occur in the absence of cell division, resulting in a rapid depletion of the epidermal stem cell pool. We thus demonstrate that the epidermis is maintained by nearest neighbor coordination of cell fates, rather than by asymmetric divisions or fine-tuned cell-autonomous stochastic fate choices. These findings establish differentiation-dependent division as a core feature of homeostatic control, and define the relevant time and length scales over which homeostasis is enforced in epithelial tissues.

cell biology↗

bioRxiv2017

Proteomics of phosphorylation and protein dynamics during fertilization and meiotic exit in the Xenopus egg

Fertilization triggers release from meiotic arrest and initiates events that prepare for the ensuing developmental program. Protein degradation and phosphorylation are known to regulate protein activity during this process. However, the full extent of protein loss and phospho-regulation is still unknown. We examined absolute protein and phospho-site dynamics after fertilization by mass spectrometry-based proteomics. To do this, we developed a new approach for calculating the stoichiometry of phospho-sites from multiplexed proteomics that is compatible with dynamic, stable and multi-site phosphorylation. Overall, the data suggest that degradation is limited to a few low abundance proteins. However, this degradation promotes extensive dephosphorylation that occurs over a wide range of abundances during meiotic exit. We also show that eggs release a large amount of protein into the medium just after fertilization, most likely related to the blocks to polyspermy. Concomitantly, there is a substantial increase in phosphorylation likely tied to calcium activated kinases. We identify putative degradation targets as well as new components of the block to polyspermy. The analytical approaches demonstrated here are broadly applicable to studies of dynamic biological systems.\n\nSignificance StatementProtein phosphorylation and degradation drive critical events in early embryogenesis and the cell cycle; however, comprehensive and accurate analysis of these changes is currently difficult. Using a mass spectrometric approach, we present a quantitative view of the protein and posttranslational economy of the fertilization response in the frog egg. Protein degradation affects a small but very important class of proteins, while regulatory phosphorylation and protein release occur on a far larger scale. We have developed new, broadly applicable analytical methods for phosphorylation that provide absolute quantification with confidence intervals for improved interpretability of post-translational modification analysis.

systems biology↗