Biology subjects

Welch, J. D.

Publications and source records attributed to Welch, J. D..

33 records · Page 2Linked to original sources

bioRxiv2023

CytoSimplex: Visualizing Single-cell Fates and Transitions on a Simplex

SummaryCells differentiate to their final fates along unique trajectories, often involving multi-potent progenitors that can produce multiple terminally differentiated cell types. Recent developments in single-cell transcriptomic and epigenomic measurement provide tremendous opportunities for mapping these trajectories. The visualization of single-cell data often relies on dimension reduction methods such as UMAP to simplify high-dimensional single-cell data down into an understandable two-dimensional (2D) form. However, these visualization methods can be misleading and often do not effectively represent the direction of cell differentiation. To address these limitations, we developed a new approach that places each cell from a single-cell dataset within a simplex whose vertices correspond to terminally differentiated cell types. Our approach can quantify and visualize current cell fate commitment and future cell potential. We developed CytoSimplex, a standalone open-source package implemented in R and Python that provides simple and intuitive visualizations of cell differentiation in 2D ternary and three-dimensional (3D) quaternary plots. We believe that CytoSimplex can help researchers gain a better understanding of cell type transitions in specific tissues and characterize developmental processes. Availability and implementationThe R version of CytoSimplex is available on Github at https://github.com/welch-lab/CytoSimplex. The Python version of CytoSimplex is available on Github at https://github.com/welch-lab/pyCytoSimplex.

bioinformatics↗

bioRxiv2023

HIV-1 Vpr combats the PU.1-driven antiviral response in primary human macrophages.

HIV-1 Vpr promotes efficient spread of HIV-1 from macrophages to T cells by transcriptionally downmodulating restriction factors that target HIV-1 Envelope protein (Env). Here we find that Vpr induces broad transcriptomic changes by targeting PU.1, a transcription factor necessary for expression of host innate immune response genes, including those that target Env. Consistent with this, we find silencing PU.1 in infected macrophages lacking Vpr rescues Env. Vpr downmodulates PU.1 through a proteasomal degradation pathway that depends on physical interactions with PU.1 and DCAF1, a component of the Cul4A E3 ubiquitin ligase. The capacity for Vpr to target PU.1 is highly conserved across primate lentiviruses. In addition to impacting infected cells, we find that Vpr suppresses expression of innate immune response genes in uninfected bystander cells, and that virion-associated Vpr can degrade PU.1. Together, we demonstrate Vpr counteracts PU.1 in macrophages to blunt antiviral immune responses and promote viral spread.

microbiology↗

bioRxiv2022

MorphNet Predicts Cell Morphology from Single-Cell Gene Expression

Gene expression and morphology both play a key role in determining the types and functions of cells, but the relationship between molecular and morphological features is largely uncharacterized. We present MorphNet, a computational approach that can draw pictures of a cells morphology from its gene expression profile. Our approach leverages paired morphology and molecular data to train a neural network that can predict nuclear or whole-cell morphology from gene expression. We employ state-of-the-art data augmentation techniques that allow training using as few as 103 images. We find that MorphNet can generate novel, realistic morphological images that retain the complex relationship between gene expression and cell appearance. We then train MorphNet to generate nuclear morphology from gene expression using brain-wide MERFISH data. In addition, we show that MorphNet can generate neuron morphologies with realistic axonal and dendritic structures. MorphNet generalizes to unseen brain regions, allowing prediction of neuron morphologies across the entire mouse isocortex and even non-cortical regions. We show that MorphNet performs meaningful latent space interpolation, allowing prediction of the effects of gene expression variation on morphology. Finally, we provide a web server that allows users to predict neuron morphologies for their own scRNA-seq data. MorphNet represents a powerful new approach for linking gene expression and morphology.

bioinformatics↗

bioRxiv2022

PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations

Small molecule treatment and gene knockout or overexpression induce complex changes in the molecular states of cells, and the space of possible perturbations is too large to measure exhaustively. We present PerturbNet, a deep generative model for predicting the distribution of cell states induced by unseen chemical or genetic perturbations. Our key innovation is to use high-throughput perturbation response data such as Perturb-Seq to learn a continuous mapping between the space of possible perturbations and the space of possible cell states. Using Sci-Plex and LINCS datasets, PerturbNet can accurately predict the distribution of gene expression changes induced by unseen small molecules given only their chemical structures. PerturbNet also accurately predicts gene expression changes induced by shRNA, CRISPRi, or CRISPRa perturbations using a perturbation network trained on gene functional annotations. Furthermore, self-supervised sequence embeddings allow PerturbNet to predict gene expression changes induced by missense mutations. We also use PerturbNet to attribute cell state shifts to specific perturbation features, including atoms and functional gene annotations. Finally, we leverage PerturbNet to design perturbations that achieve a desired cell state distribution. PerturbNet holds great promise for understanding perturbation responses and ultimately designing novel chemical and genetic interventions.

bioinformatics↗

bioRxiv2022

Bayesian Inference of RNA Velocity from Multi-Lineage Single-Cell Data

Experimental approaches for measuring single-cell gene expression can observe each cell at only one time point, requiring computational approaches for reconstructing the dynamics of gene expression during cell fate transitions. RNA velocity is a promising computational approach for this problem, but existing inference methods fail to capture key aspects of real data, limiting their utility. To address these limitations, we developed VeloVAE, a Bayesian model for RNA velocity inference. VeloVAE uses variational Bayesian inference to estimate the posterior distribution of latent time, latent cell state, and kinetic rate parameters for each cell. Our approach addresses key limitations of previous methods by inferring a global time and cell state value for each cell; explicitly modeling the emergence of multiple cell types; incorporating prior information such as time point labels; using scalable minibatch optimization; and quantifying parameter uncertainty. We show that VeloVAE significantly outperforms previous approaches in terms of data fit and accuracy of inferred differentiation directions. VeloVAE can also capture qualitative features of expression dynamics neglected by previous methods, including late induction, early repression, transcriptional boosts, and bifurcations. These improvements allow VeloVAE to accurately model gene expression dynamics in complex biological systems, including hematopoiesis, induced pluripotent stem cell reprogramming, neurogenesis, and organogenesis. We find that the latent time automatically inferred using all cells can even outperform pseudotime inferred using manually chosen cell subsets and root cells. We further use the inferred parameters to construct cell type transition graphs and fit branching differential equation models that describe the effects of cell type bifurcations on kinetic rate parameters.

bioinformatics↗

bioRxiv2021

PyLiger: Scalable single-cell multi-omic data integration in Python

MotivationLIGER is a widely-used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly-optimized scientific computing libraries for increased efficiency. ResultsWe developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2-5x speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability, and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory. AvailabilityPyLiger is available on Github at https://github.com/welch-lab/pyliger and on the Python Package Index. Contactwelchjd@umich.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Single-cell multi-omic velocity infers dynamic and decoupled gene regulation

Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide a unique opportunity to discover the relationships between cellular epigenomic and transcriptomic changes. To realize this potential, we developed MultiVelo, a mechanistic model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression from single-cell data, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes. Incorporating chromatin accessibility data significantly improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Fitting MultiVelo on single-cell multi-omic datasets from brain, skin, and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. Our model also identifies four types of cell states-two states in which epigenome and transcriptome are coupled and two distinct decoupled states. The parameters inferred by MultiVelo quantify the length of time for which genes occupy each of the four states, ranking genes by the degree of coupling between transcriptome and epigenome. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. We provide an open-source Python implementation of MultiVelo on PyPI and GitHub (https://github.com/welch-lab/MultiVelo).

bioinformatics↗

bioRxiv2021

Nonnegative matrix factorization integrates single-cell multi-omic datasets with partially overlapping features

Single-cell genomic technologies provide an unprecedented opportunity to define molecular cell types in a data-driven fashion, but present unique data integration challenges. Integration analyses often involve datasets with partially overlapping features, including both shared features that occur in all datasets and features exclusive to a single experiment. Previous computational integration approaches require that the input matrices share the same number of either genes or cells, and thus can use only shared features. To address this limitation, we derive a novel nonnegative matrix factorization algorithm for integrating single-cell datasets containing both shared and unshared features. The key advance is incorporating an additional metagene matrix that allows unshared features to inform the factorization. We demonstrate that incorporating unshared features significantly improves integration of single-cell RNA-seq, spatial transcriptomic, SHARE-seq, and cross-species datasets. We have incorporated the UINMF algorithm into the open-source LIGER R package (https://github.com/welch-lab/liger).

bioinformatics↗

bioRxiv2021

Sampling from Disentangled Representations of Single-Cell Data Using Generative Adversarial Networks

Deep generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), have achieved remarkable successes in generating and manipulating highdimensional images. VAEs excel at learning disentangled image representations, while GANs excel at generating realistic images. Here, we systematically assess disentanglement and generation performance on single-cell gene expression data and find that these strengths and weaknesses of VAEs and GANs apply to single-cell gene expression data in a similar way. We also develop MichiGAN1, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of two large singlecell RNA-seq datasets [13, 68] and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.

bioinformatics↗

bioRxiv2021

Real-Time, Direct Classification of Nanopore Signals with SquiggleNet

Oxford Nanopore sequencers provide results in real time as DNA passes through a nanopore and can eject a molecule after it has been partly sequenced. However, the computational challenge of deciding whether to keep or reject a molecule in real time has limited the application of this capability. We present SquiggleNet, the first deep learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than the DNA passes through the pore, allowing real-time classification and read ejection. When given the amount of sequencing data generated in one second, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than approaches based on alignment. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen species, identified bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

bioinformatics↗

bioRxiv2020

Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGER

High-throughput single-cell sequencing technologies hold tremendous potential for defining cell types in an unbiased fashion using gene expression and epigenomic state. A key challenge in realizing this potential is integrating single-cell datasets from multiple protocols, biological contexts, and data modalities into a joint definition of cellular identity. We previously developed an approach called Linked Inference of Genomic Experimental Relationships (LIGER) that uses integrative nonnegative matrix factorization to address this challenge. Here, we provide a step-by-step protocol for using LIGER to jointly define cell types from multiple single-cell datasets. The main steps of the protocol include data preprocessing and normalization, joint factorization, quantile normalization and joint clustering, and visualization. We describe how to jointly define cell types from single-cell RNA-seq and single-nucleus ATAC-seq data, but similar steps apply across a wide range of other settings and data types, including cross-species analysis, single-nucleus DNA methylation, and spatial transcriptomics. Our protocol contains examples of expected results, describes common pitfalls, and relies only on our freely available, open-source R implementation of LIGER. We also provide Rmarkdown tutorials showing the outputs from each individual code segment. The analysis process can be performed in 1 - 4 h depending on dataset size and assumes no specialized bioinformatics training.

bioinformatics↗

bioRxiv2020

Notch effector Hes1 marks an early perichondrial population of skeletal progenitor cells at the onset of endochondral bone development

The perichondrium, a fibrous tissue surrounding the fetal cartilage, is an essential component of developing endochondral bones that provides a source of skeletal progenitor cells. However, perichondrial cells remain poorly characterized due to lack of knowledge on their cellular diversity and subset-specific mouse genetics tools. Single cell RNA-seq analyses reveal a contiguous nature of the fetal chondrocyte-perichondrial cell lineage that shares an overlapping set of marker genes. Subsequent cell-lineage analyses using multiple creER lines active in fetal perichondrial cells - Hes1-creER, Dlx5-creER - and chondrocytes - Fgfr3-creER - illustrate their distinctive contribution to endochondral bone development; postnatally, these cells contribute to the functionally distinct bone marrow stromal compartments. Particularly, Notch effector Hes1-creER marks an early skeletal progenitor cell population in the primordium that robustly populates multiple skeletal compartments. These findings support the concept that perichondrial cells participate in endochondral bone development through a distinct route, by providing a complementary source of skeletal progenitor cells.

developmental biology↗

bioRxiv2020

Single-Cell Analysis of the 3D Topologies of Genomic Loci Using Genome Architecture Mapping

Although each cell within an organism contains a nearly identical genome sequence, the three-dimensional (3D) packing of the genome varies among individual cells, influencing cell-type-specific gene expression. Genome Architecture Mapping (GAM) is the first genome-wide experimental method for capturing 3D proximities between any number of genomic loci without ligation. GAM overcomes several limitations of 3C-based methods by sequencing DNA from a large collection of thin sections sliced from individual nuclei. The GAM technique measures locus co-segregation, extracts radial positions, infers chromatin compaction, requires small numbers of cells, does not depend on ligation, and provides rich single-cell information. However, previous analyses of GAM data focused exclusively on population averages, neglecting the variation in 3D topology among individual cells. We present the first single-cell analysis of GAM data, demonstrating that the slices from individual cells reveal intercellular heterogeneity in chromosome conformation. By simultaneously clustering both slices and genomic loci, we identify topological variation among single cells, including differential compaction of cell cycle genes. We also develop a geometric model of the nucleus, allowing prediction of the 3D positions of each slice. Using GAM data from mouse embryonic stem cells, we make new discoveries about the structure of the major mammalian histone gene locus, which is incorporated into the Histone Locus Body (HLB), including structural fluctuations and putative causal molecular mechanisms. Our methods are packaged as SluiceBox, a toolkit for mining GAM data. Our approach represents a new method of investigating variation in 3D genome topology among individual cells across space and time.

bioinformatics↗

bioRxiv2020

Iterative Refinement of Cellular Identity from Single-Cell Data Using Online Learning

Recent experimental advances have enabled high-throughput single-cell measurement of gene expression, chromatin accessibility and DNA methylation. We previously used integrative non-negative matrix factorization (iNMF) to jointly learn interpretable low-dimensional representations from multiple single-cell datasets using dataset-specific and shared metagene factors. These factors provide a principled, quantitative definition of cellular identity and how it varies across biological contexts. However, datasets exceeding 1 million cells are now widely available, creating computational barriers to scientific discovery. For instance, it is no longer feasible to analyze large datasets using standard pipelines on a personal computer with limited memory capacity. Moreover, there is a need for an algorithm capable of iteratively refining the definition of cellular identity as efforts to create a comprehensive human cell atlas continually sequence new cells. To address these challenges, we developed an online learning algorithm for integrating large and continually arriving single-cell datasets. We extended previous online learning approaches for NMF to minimize the expected cost of a surrogate function for the iNMF objective. We also derived a novel hierarchical alternating least squares algorithm for iNMF and incorporated it into an efficient online algorithm. Our online approach accesses the training data as mini-batches, decoupling memory usage from dataset size and allowing on-the-fly incorporation of new datasets as they are generated. The online implementation of iNMF converges much more quickly using a fraction of the memory required for the batch implementation, without sacrificing solution quality. Our new approach processes 1.3 million single cells from the entire mouse embryo on a laptop in 25 minutes using less than 500 MB of RAM. We also analyze large datasets without downloading them to disk by streaming them over the internet on demand. Furthermore, we construct a single-cell multi-omic cell atlas of the mouse motor cortex by iteratively incorporating eight single-cell RNA-seq, single-nucleus RNA-seq, single-nucleus ATAC-seq, and single-nucleus DNA methylation datasets generated by the BRAIN Initiative Cell Census Network. Our approach obviates the need to recompute results each time additional cells are sequenced, dramatically increases convergence speed, and allows processing of datasets too large to fit in memory or on disk. Most importantly, it facilitates continual refinement of cell identity as new single-cell datasets from different biological contexts and data modalities are generated.

bioinformatics↗

bioRxiv2017

Manifold Alignment Reveals Correspondence Between Single Cell Transcriptome and Epigenome Dynamics

Single cell genomic techniques promise to yield key insights into the dynamic interplay between gene expression and epigenetic modification. However, the experimental difficulty of performing multiple measurements on the same cell currently limits efforts to combine multiple genomic data sets into a united picture of single cell variation. We show that it is possible to construct cell trajectories, reflecting the changes that occur in a sequential biological process, from single cell ATAC-seq, bisulfite sequencing, and ChIP-seq data. In addition, we present an approach called MATCHER that computationally circumvents the experimental difficulties inherent in performing multiple genomic measurements on a single cell by inferring correspondence between single cell transcriptomic and epigenetic measurements performed on different cells of the same type. MATCHER works by first learning a separate manifold for the trajectory of each kind of genomic data, then aligning the manifolds to infer a shared trajectory in which cells measured using different techniques are directly comparable. Using scM&T-seq data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondence information. We also used MATCHER to infer correlations among gene expression, chromatin accessibility, and histone modifications in single mouse embryonic stem cells. These results reveal the dynamic interplay between epigenetic changes and gene expression underlying the transition from pluripotency to differentiation priming. Our work is a first step toward a united picture of heterogeneous transcriptomic and epigenetic states in single cells.

bioinformatics↗