Biology subjects

Streets, A.

Publications and source records attributed to Streets, A..

11 recordsLinked to original sources

bioRxiv2025

Integrated analysis of multimodal long-read epigenetic assays

Long-read sequencing assays that detect base modifications are becoming increasingly important research tools for the study of epigenetic regulation, especially with the development of DiMeLo-seq and similar methods that deposit non-native base modifications to mark a range of epigenetic features such as protein-DNA interactions and chromatin accessibility. A main benefit of these methods is their inherent capacity for multimodality, enabling the encoding of multiple genomic signals onto single nucleic acid molecules. However, there are limited tools available for visualization and statistical analysis of this type of multimodal data. Here we introduce dimelo-toolkit, a python package built to enable flexible visualizations and easy integration into custom data processing workflows. We demonstrate the utility of dimelo-toolkits preset visualizations of multiple base modifications in long-read single-molecule sequencing data with a novel extension of the DiMeLo-seq protocol that can capture three separate aspects of chromatin state on the same single reads: target protein binding, CpG methylation, and chromatin accessibility. We apply this multimodal method to simultaneously map chromatin accessibility, CpG methylation, and LMNB1 and CTCF binding patterns, respectively, in GM12878 cells. Our flexible design allows us to investigate previously unexplored technical biases that arise when working with this type of multimodal data. Additionally, we show that dimelo-toolkit enables analysis for a wide range of other long-read sequencing methods, such as mapping endogenous patterns in RNA base modifications with direct RNA sequencing. This software tool will pave the way for developing well-optimized protocols and help unlock previously inaccessible biological insights.

genomics↗

bioRxiv2025

DiMeLo-cito: a one-tube protocol for mapping protein-DNA interactions reveals CTCF bookmarking in mitosis

Genome regulation relies on complex and dynamic interactions between DNA and proteins. Recently, powerful methods have emerged that leverage third-generation sequencing to map protein-DNA interactions genome-wide. For example, Directed Methylation with Long-read sequencing (DiMeLo-seq) enables mapping of protein-DNA interactions along long, single chromatin fibers, including in highly repetitive genomic regions. However, DiMeLo-seq involves lossy centrifugation-based wash steps that limit its applicability to many sample types. To address this, we developed DiMeLo-cito, a single-tube, wash-free protocol that maximizes the yield and quality of genomic DNA obtained for long-read sequencing. This protocol enables the interrogation of genome-wide protein binding with as few as 100,000 cells and without the requirement of a nuclear envelope, enabling confident measurement of protein-DNA interactions during mitosis. Using this protocol, we detected strong binding of CTCF to mitotic chromosomes in diploid human cells, in contrast with earlier studies in karyotypically unstable cancer cell lines, suggesting that CTCF "bookmarks" specific sites critical for maintaining genome architecture across cell divisions. By expanding the capabilities of DiMeLo-seq to a broader range of sample types, DiMeLo-cito can provide new insights into genome regulation and organization.

genomics↗

bioRxiv2024

Scvi-hub: an actionable repository for model-driven single cell analysis

The accumulation of single-cell omics datasets in the public domain has opened new opportunities for reusing and leveraging the vast amount of information they contain. Such uses, however, are complicated by the need for complex and resource-consuming procedures for data transfer, normalization and integration that must be addressed prior to any analysis. Here we present scvi-hub: a platform for efficiently sharing and accessing single-cell omics datasets using pre-trained probabilistic models. We demonstrate that scvi-hub allows immediate access to a slew of fundamental tasks like visualization, imputation, annotation, outlier detection, and deconvolution of new (query) datasets, using state of the art algorithms and with a requirement for storage and compute resources that is much lower compared to standard approaches. We also show that the pre-trained models enable efficient analysis and new discoveries with existing references, including large atlases such as the CZ CELLxGENE Discover Census. Scvi-hub is built within the scvi-tools open source environment and integrated into scverse. It provides powerful and readily available tools for utilizing a large collection of already-loaded datasets while also enabling easy inclusion of new datasets, thus putting the power of atlas-level analysis at the fingertips of a broad community of users.

bioinformatics↗

bioRxiv2021

Multimodal measurements of phototoxicity in nonlinear optical microscopy

Nonlinear optical imaging modalities, such as two-photon microscopy and stimulated Raman scattering (SRS) microscopy, make use of pulsed-laser excitation with high peak intensity that can perturb the native state of cells. In this study, we investigated the short and long-term effects of pulsed laser induced phototoxicity. We used bulk RNA sequencing, quantitative measurement of cell proliferation, and measurement of the generation of reactive oxygen species (ROS) to assess phototoxic effects, at different time scales, for a range of laser excitation settings relevant to SRS imaging. We define a range of laser excitation settings for which there was no significant ROS generation, differential gene expression, or change in proliferation rates of mouse Neuro2A cells. Changes in proliferation rate and ROS generation were observed under imaging conditions with an excitation intensity of over 600 mW/m2. Repeated imaging of the same field of view at this excitation intensity of over 600 mW/m2 resulted in visual damage to N2A cells. Laser induced perturbations in live cells may impact downstream measurements of cell state including subsequent imaging or molecular measurements. This study provides guidance for imaging parameters that minimize photo-induced perturbations in SRS microscopy to ensure accurate interpretation of experiments with time-lapse imaging or with paired measurements of imaging and sequencing on the same cells.

bioengineering↗

bioRxiv2021

Single-cell multi-omic analysis of thymocyte development reveals NFAT as adriver of CD4/CD8 lineage commitment

The development of CD4 and CD8 T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-MHCI/II by the T cell antigen receptor (TCR) determines the lineage choice, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. We applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and lineage-restricted mice to generate a comprehensive timeline of cell state for each lineage. These analyses revealed a sequential process whereby all thymocytes initiate CD4 lineage differentiation during an initial wave of TCR signaling, followed by a second TCR signaling wave that coincides with CD8 lineage specification. CITE-seq and pharmaceutical inhibition experiments implicate a TCR/calcineurin/NFAT/GATA3 axis in driving the CD4 fate. Overall, our data suggest that multiple redundant mechanisms contribute to the accuracy and efficiency of the lineage choice.

bioinformatics↗

bioRxiv2021

DiMeLo-seq: a long-read, single-molecule method for mappingprotein-DNA interactions genome-wide

Molecular studies of genome regulation often rely on the ability to map where specific proteins interact with genomic DNA. Existing techniques for mapping protein-DNA interactions genome-wide rely on DNA amplification methods followed by sequencing with short reads, which dissociates joint binding information at neighboring sites, removes endogenous DNA methylation information, and precludes the ability to reliably map interactions in repetitive regions of the genome. To address these limitations, we created a new protein-DNA mapping method, called Directed Methylation with Long-read sequencing (DiMeLo-seq), which methylates DNA near each target proteins DNA binding site in situ, then leverages the ability to distinguish methylated and unmethylated bases on long, native DNA molecules using long-read, single-molecule sequencing technologies. We demonstrate the optimization and utility of this method by mapping the interaction sites of a variety of different proteins and histone modifications across the human genome, achieving a single-molecule binding site resolution of less than 200 bp. Furthermore, we mapped the positions of the centromeric histone H3 variant CENP-A in repetitive regions that are unmappable with short reads, while simultaneously analyzing endogenous CpG methylation and joint binding events on single molecules. DiMeLo-seq is a versatile method that can provide multimodal and truly genome-wide information for investigating protein-DNA interactions.

genomics↗

bioRxiv2021

scvi-tools: a library for deep probabilistic analysis of single-cell omics data

AO_SCPLOWBSTRACTC_SCPLOWProbabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

bioinformatics↗

bioRxiv2021

T cell self-reactivity during thymic development dictates the timing of positive selection

Functional tuning of T cells based on their degree of self-reactivity is established during positive selection in the thymus, although how positive selection differs for thymocytes with relatively low versus high self-reactivity is unclear. In addition, preselection thymocytes are highly sensitive to low-affinity ligands, but the mechanism underlying their enhanced TCR sensitivity is not fully understood. Here we show that murine thymocytes with low self-reactivity experience briefer TCR signals and complete positive selection more slowly than those with high self-reactivity. Additionally, we provide evidence that cells with low self-reactivity retain a preselection gene expression signature as they mature, including genes previously implicated in modulating TCR sensitivity and a novel group of ion channel genes. Our results imply that thymocytes with low self-reactivity down-regulate TCR sensitivity more slowly during positive selection, and suggest that modulation of membrane ion channel function may play a role in regulating TCR tuning throughout development. Impact StatementDeveloping T cells whose TCRs have relatively low reactivity experience very brief TCR signaling events, delayed positive selection, and do not fully down regulate their TCR sensitivity as they mature.

developmental biology↗

bioRxiv2020

Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells

AO_SCPLOWBSTRACTC_SCPLOWThe paired measurement of RNA and surface protein abundance in single cells with CITE-seq is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, each data modality exhibits unique technical biases, making it challenging to conduct a joint analysis and combine these two views into a unified representation of cell state. Here we present Total Variational Inference (totalVI), a framework for the joint probabilistic analysis of paired RNA and protein data from single cells. totalVI probabilistically represents the data as a composite of biological and technical factors such as limited sensitivity of the RNA data, background in the protein data, and batch effects. To evaluate totalVI, we performed CITE-seq on immune cells from murine spleen and lymph nodes with biological replicates and with different antibody panels measuring over 100 surface proteins. With this dataset, we demonstrate that totalVI provides a cohesive solution for common analysis tasks like the integration of datasets with matched or unmatched protein panels, dimensionality reduction, clustering, evaluation of correlations between molecules, and differential expression testing. totalVI enables scalable, end-to-end analysis of paired RNA and protein data from single cells and is available as open-source software.

bioinformatics↗

bioRxiv2020

μCB-seq: Microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells

Single-cell RNA sequencing (scRNA-seq) enables the investigation of complex biological processes in multicellular organisms with high resolution. However, many phenotypic features that are critical to understanding the functional role of cells in a heterogeneous tissue or organ are not directly encoded in the genome and therefore cannot be profiled with scRNA-seq. Quantitative optical microscopy has long been a powerful approach for characterizing diverse cellular phenotypes including cell morphology, protein localization, and chemical composition. Combining scRNA-seq with optical imaging has the potential to provide comprehensive single-cell analysis, allowing for functional integration of gene expression profiling and cell-state characterization. However, it is difficult to track single cells through both measurements; therefore, coupling current scRNA-seq protocols with optical measurements remains a challenge. Here, we report Microfluidic Cell Barcoding and Sequencing (CB-seq), a microfluidic platform that combines high-resolution imaging and sequencing of single cells. CB-seq is enabled by a novel fabrication method that preloads primers with known barcode sequences inside addressable reaction chambers of a microfluidic device. In addition to enabling multi-modal single-cell analysis, CB-seq improves gene detection sensitivity, providing a scalable and accurate method for information-rich characterization of single cells.

bioengineering↗

bioRxiv2019

Hunting strategies and the lateral line of Astyanax mexicanus larva

The lateral line is the primary modality fish use to create a hydrodynamic image of their environment. These images contribute to a variety of behaviors, from rheotaxis to escape responses. Here we discern the contributions of visual and lateral line modalities in hunting behavior of larvae that have developed under different photic conditions. In particular, cave animals have a hypertrophied sense of mechanosensation, and we studied the common animal model cavefish Astyanax mexicanus and its closest related surface relative. We raised larvae in a diurnal light-dark regimen and in complete darkness. We then examined the distribution of neuromasts in their lateral lines, and their hunting performance in light and dark conditions, with and without the contribution of the lateral line. We report that all larva depend on the lateral line for success in hunting and that surface fish raised in the dark have a greater dependency on the lateral line,

evolutionary biology↗