Biology subjects

Streets, A. M.

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

At least 19 recordsLinked to original sources

bioRxiv2026

Denoising single-cell ATAC-seq data with Shannon entropy

Single-cell Assay for Transposase Accessible Chromatin with sequencing (scATAC-seq) is widely used to investigate chromatin accessibility but experiments often contain empty droplets, droplets containing damaged nuclei or cellular-debris. Accurately filtering high-quality nuclei is important as noisy droplets contribute reads that can distort downstream analysis and obscure rare cell population. Current approaches often rely on read depth and or predefined genomic features that are not exactly the same as chromatin accessibility landscape. Here, we present a Shannon entropy-based method that filters high-quality nuclei more robustly by quantifying genome-wide accessibility complexity. This approach improves signal-to-noise ratio, increases sensitivity for accessible-region detection, and enables more reliable downstream inference in scATAC-seq studies.

bioinformatics↗

bioRxiv2026

Epigenetic conditioning improves sequence-based modeling of gene regulation across cell types and alleles

Epigenetic state modulates gene regulation in a manner not always predictable from DNA sequence alone, yet current genomic deep learning models do not leverage epigenetic state as input. We present MethylSeqNet, a model that conditions pretrained sequence embeddings on CpG methylation, a stable epigenetic mark increasingly available from long-read sequencing data. Using a novel conditioning mechanism enabling scalability and interpretability, MethylSeqNet improves predictions in cases where differential epigenetic state drives regulatory variation. We show improvements over a sequence-only baseline for cell-type-specific chromatin accessibility and transcription. Epigenetic conditioning enables prediction of phenomena not encoded in allele sequence, including parent-of-origin imprinting, random monoallelic activity, and X-inactivation. We highlight a promising application of methylation conditioning by predicting the effects of a structural rearrangement in one rare disease patient case study. In silico motif insertion analysis confirms that MethylSeqNet learns methylation-dependent regulatory grammar, establishing a paradigm for integrating epigenetic information into genomic deep learning with immediate applications in rare disease interpretation.

genomics↗

bioRxiv2026

Token Alignment for Verifying LLM-Extracted Text

Large language models excel at text extraction, but they sometimes hallucinate. A simple way to avoid hallucinations is to remove any extracted text that does not appear in the original source. This is easy when the extracted text is contiguous (findable with exact string matching), but much harder when it is discontiguous. Techniques for finding discontiguous phrases depend heavily on how the text is split--i.e., how it is tokenized. In this study, we show that splitting text along subword boundaries, with LLM-specific tokenizers, and aligning extracted text with ordered alignment algorithms, improves alignment by about 50% compared to word-level tokenization. To demonstrate this, we introduce the Berkeley Ordered Alignment of Text (BOAT) dataset, a modification of the Stanford Question Answering Dataset (SQuAD) that includes non-contiguous phrases, and BIO-BOAT a biomedical variant built from 51 bioRxiv preprints. We show that text-alignment methods form a partially ordered set, and that ordered alignment is the most practical choice for verifying LLM-extracted text. We implement this approach in taln, which enumerates ordinal subword alignments.

bioinformatics↗

bioRxiv2025

Effects of biological fixation methods on stimulated Raman scattering microscopy signal

Stimulated Raman Scattering (SRS) microscopy enables label-free imaging of cells and tissues in their native state with chemical specificity. However, there are often experimental advantages of chemical fixation of samples prior to imaging, which can introduce perturbations that may alter the native state of the samples, and possibly impact the SRS signal. In this study, we systematically characterize the effects of common fixatives (Paraformaldehyde, Formalin, Glutaraldehyde, Methanol, Ethanol) on the preservation of cellular integrity and molecular composition in a Hela cell model as observed by SRS microscopy. We demonstrate how the different fixatives can influence lipid and protein content, and overall cell morphology, with significant implications for the accuracy of quantitative SRS microscopy. Our findings indicate that Paraformaldehyde (PFA) imposes minimal disruption to cellular and molecular states compared to the other fixatives tested, and suggest Glutaraldehyde (GA) as a suitable alternative for SRS imaging. This study provides insights for the choice of the optimal sample preparation, enabling more reliable SRS-based studies for the evaluation of cellular processes and disease mechanisms where fixation is used.

cell biology↗

bioRxiv2025

Preselection CD4+CD8+ thymocytes modulate TCR responsiveness following TCRβ selection

Modulation of TCR sensitivity during positive selection is critical to avoid negative selection and direct thymocytes into their appropriate lineage. Thymocytes just prior to positive selection (preselection) are highly responsive to low affinity self-ligands and are also actively rearranging their TCR locus as they await a positive selection signal. Preselection DP thymocytes were thought to be relatively homogeneous and TCR modulation during this stage had not been previously described. Here we provide evidence for progressive gene expression changes within the preselection DP thymocyte population that correlates with a gradual loss of TCR responsiveness and a defect in upregulating TCR target genes associated with the CD4 fate. We relate these observations to the link between positive selection and T cell lineage commitment.

immunology↗

bioRxiv2025

Single-Nucleus Analysis of Human White Adipose Tissue Reveals Adipocyte Subsets with Distinct Metabolic Profiles

Anatomic location of white adipose tissue is a determinant of cardiometabolic risk. To understand differences within/between adipose depots, we generated 65,668 single-nucleus transcriptomes from human subcutaneous or intraabdominal adipose tissue (SAT/IAT). Unsupervised analysis revealed 26 adipose-resident cell clusters including two subpopulations of mature adipocytes, characterized by high vs. low expression of adipocyte maturation genes (ADIPOMAThi vs. ADIPOMATlo). ADIPOMATlo adipocytes demonstrate a low-differentiation, pro-inflammatory, and pro-fibrotic transcriptome. IAT-resident ADIPOMATlo were more abundant in higher BMI donors, while SAT-resident ADIPOMATlo associated with impaired glycemia. TSHZ3 was identified as a candidate regulator of ADIPOMATlo transcriptome. TSHZ3 knockdown in adipogenic progenitors inhibited differentiation, with downregulation of early adipogenic regulators (e.g. CEBPA/B, PPARG) and mature adipocyte genes. Heterozygous deletion of Tshz3 in mice reduced SAT and IAT weight. Here, we show that adipocyte subsets with distinct transcriptomic signature reside in human WAT; altered TSHZ3-mediated transcriptional regulation may contribute to low-maturation subpopulation linked to metabolic disease.

cell biology↗

bioRxiv2025

SRS microscopy identifies inhibition of vitellogenesis as a mediator of lifespan extension by caloric restriction in C. elegans

The molecular mechanisms of aging are not fully understood. Here, we used label-free Stimulated Raman scattering (SRS) microscopy to investigate changes in proteins and lipids throughout the lifespan of C. elegans. We observed a dramatic buildup of proteins within the body cavity or pseudocoelom of aged adults that was blunted by interventions that extend lifespan: caloric restriction (CR) and the reduced insulin/insulin-like growth factor signaling (IIS) pathway. Using a combination of microscopy, proteomic analysis, and validation with mutant strains, we identified vitellogenins as the key molecular components of the protein buildup in the pseudocoelom. Vitellogenins shuttle nutrients from intestine to embryos and are homologous to human apolipoprotein B, the causal driver of cardiovascular disease. We then showed that CR and knockdown of vitellogenins both extend lifespan by >60%, but their combination has no additional effect on lifespan, suggesting that CR extends the lifespan of C. elegans in part by inhibiting vitellogenesis. The extensive dataset of more than 12,000 images stitched into over 350 whole-animal SRS images of C. elegans at different ages and subjected to different longevity intervention will be a valuable resource for researchers interested in aging.

cell biology↗

bioRxiv2023

Consensus prediction of cell type labels with popV

Cell-type classification is a crucial step in single-cell analysis. To facilitate this, several methods have been proposed for the task of transferring a cell-type label from an annotated reference atlas to unannotated query data sets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV, https://github.com/YosefLab/popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides effective uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process.

bioinformatics↗

bioRxiv2022

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells

RNA velocity has been rapidly adopted to guide the interpretation of transcriptional dynamics in snapshot single-cell transcriptomics data. Current approaches for estimating and analyzing RNA velocity can empirically reveal complex dynamics but lack effective strategies for quantifying the uncertainty of the estimate and its overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show in a series of examples that veloVI compares favorably to previous approaches for inferring RNA velocity with improvements in fit to the data, consistency across transcriptionally similar cells, and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that properties unique to veloVI, such as posterior velocity uncertainty, can be used to assess the appropriateness of analysis with velocity to the data at hand. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.

bioinformatics↗

bioRxiv2022

Mapping protein-DNA interactions with DiMeLo-seq

We recently developed Directed Methylation with Long-read sequencing (DiMeLo-seq) to map protein-DNA interactions genome wide. DiMeLo-seq is capable of mapping multiple interaction sites on single DNA molecules, profiling protein binding in the context of endogenous DNA methylation, identifying haplotype specific protein-DNA interactions, and mapping protein-DNA interactions in repetitive regions of the genome that are difficult to study with short-read methods. With DiMeLo-seq, adenines in the vicinity of a protein of interest are methylated in situ by tethering the Hia5 methyltransferase to an antibody using protein A. Protein-DNA interactions are then detected by direct readout of adenine methylation with long-read, single-molecule, DNA sequencing platforms such as Nanopore sequencing. Here, we present a detailed protocol and practical guidance for performing DiMeLo-seq. This protocol can be run on nuclei from fresh, lightly fixed, or frozen cells. The protocol requires 1-2 days for performing in situ targeted methylation, 1-5 days for library preparation depending on desired fragment length, and 1-3 days for Nanopore sequencing depending on desired sequencing depth. The protocol requires basic molecular biology skills and equipment, as well as access to a Nanopore sequencer. We also provide a Python package, dimelo, for analysis of DiMeLo-seq data. Key papersAltemose, N., Maslan, A., Smith, O.K., Sundararajan, K., Brown, R.R., Mishra, R., Detweiler, A.M., Neff, N., Miga, K.H., Straight, A.F. and Streets, A., 2022. DiMeLo-seq: a long-read, single-molecule method for mapping protein-DNA interactions genome wide. Nature Methods, pp.1-13. (https://doi.org/10.1038/s41592-022-01475-6)

genomics↗

bioRxiv2022

Mapping the temporal transcriptional landscape of human white and brown adipogenesis using single-nuclei RNA-seq

Adipogenesis is key to maintaining organism-wide energy balance and healthy metabolic phenotype, making it critical to thoroughly comprehend its molecular regulation in humans. By single-nuclei RNA-sequencing (snRNA-seq) of over 20,000 differentiating white and brown preadipocytes, we constructed a high-resolution temporal transcriptional landscape of human white and brown adipogenesis. White and brown preadipocytes were isolated from a single individuals neck region, thereby eliminating inter-subject variability across two distinct lineages. These preadipocytes were also immortalized to allow for controlled, in vitro differentiation, allowing sampling of distinct cellular states across the spectrum of adipogenic progression. Pseudotemporal cellular ordering revealed the dynamics of ECM remodeling during early adipogenesis, and lipogenic/thermogenic response during late white/brown adipogenesis. Comparison with adipogenic regulation in murine models revealed several targets for potential adipogenic/thermogenic drivers in humans. Key adipogenic and lipogenic markers revealed in our analysis were applied to analyze publicly available scRNA-seq datasets; these confirmed unique cell maturation features in recently discovered murine preadipocytes, and revealed inhibition of adipogenic expansion in humans with obesity. Overall, our study presents a comprehensive molecular description of both white and brown adipogenesis in humans and provides an important resource for future studies of adipose tissue development and function in both health and metabolic disease state.

bioengineering↗

bioRxiv2021

Colour Vision in Stomatopod Crustaceans: more questions than answers

Stomatopod crustaceans, or mantis shrimps, are known for their extensive range of spectral sensitivities but relatively poor spectral discrimination. Instead of the colour-opponent mechanism of other colour vision systems, the 12 narrow-band colour channels they possess may underlie a different method of colour processing. We investigated one hypothesis, in which the photoreceptors are proposed to act as individual wave-band detectors, interpreting colour as a parallel pattern of photoreceptor activation, rather than a ratiometric comparison of individual signals. This different form of colour detection has been used to explain previous behavioural tests in which low saturation blue was not discriminated from grey potentially because of similar activation patterns. Results here, however, indicate that the stomatopod, Haptosquilla trispinosa was able to easily distinguish several colours, including blue of both high and low saturation, from greys. The animals did show a decrease in performance over time in an artificially lit environment, indicating plasticity in colour discrimination ability. This rapid plasticity, most likely the result of a change in opsin (visual pigment) expression, has now been noted in several animal lineages (both invertebrate and vertebrate) and is a factor we suggest needing care and potential re-examination in any colour-based behavioural tests. As for stomatopods, it remains unclear why they achieve poor colour discrimination using the most comprehensive set of spectral sensitivities in the animal kingdom and also what form of colour processing they may utilise.

animal behavior and cognition↗

bioRxiv2021

Complete genomic and epigenetic maps of human centromeres

Existing human genome assemblies have almost entirely excluded highly repetitive sequences within and near centromeres, limiting our understanding of their sequence, evolution, and essential role in chromosome segregation. Here, we present an extensive study of newly assembled peri/centromeric sequences representing 6.2% (189.9 Mb) of the first complete, telomere-to-telomere human genome assembly (T2T-CHM13). We discovered novel patterns of peri/centromeric repeat organization, variation, and evolution at both large and small length scales. We also found that inner kinetochore proteins tend to overlap the most recently duplicated subregions within centromeres. Finally, we compared chromosome X centromeres across a diverse panel of individuals and uncovered structural, epigenetic, and sequence variation at single-base resolution across these regions. In total, this work provides an unprecedented atlas of human centromeres to guide future studies of their complex and critical functions as well as their unique evolutionary dynamics. One-sentence summaryDeep characterization of fully assembled human centromeres reveals their architecture and fine-scale organization, variation, and evolution.

genomics↗

bioRxiv2021

Mechanical phenotyping of acute promyelocytic leukemia reveals unique biomechanical responses in retinoic acid-resistant populations

While all-trans retinoic acid (ATRA) is an essential therapy in the treatment of acute promyelocytic leukemia (APL), an aggressive subtype of acute myeloid leukemia, nearly 20% of APL patients are resistant to ATRA. As no biomarkers for ATRA resistance yet exist, we investigated whether cell mechanics could be associated with this pathological phenotype. Using mechano-node-pore sensing, a single-cell mechanical phenotyping platform, and patient-derived APL cell lines, NB4 (ATRA-sensitive) and AP-1060 (ATRA-resistant), we discovered that ATRA-resistant APL cells are less mechanically pliable. By investigating how different subcellular components of APL cells contribute to whole-cell mechanical phenotype, we determined that nuclear mechanics strongly influence an APL cells mechanical response. By arresting APL cells in S-phase or M-phase in the cell cycle, we found cell pliability to be inversely related to DNA content. In addition to DNA content affecting cell pliability, we observed that chromatin condensation also affects nuclear mechanics: decondensing chromatin with trichostatin A is especially effective in softening ATRA-resistant APL cells. RNA-Seq allowed us to compare the transcriptomic differences between ATRA-resistant and ATRA-responsive APL cells and highlighted gene expression changes that could be associated with mechanical changes. Overall, we demonstrate the potential of "physical" biomarkers in identifying APL resistance.

bioengineering↗

bioRxiv2021

Characterization of transcript enrichment and detection bias in single-nuclei RNA-seq for mapping of distinct human adipocyte lineages

Single-cell RNA-sequencing (scRNA-seq) enables molecular characterization of complex biological tissues at high resolution. The requirement of single-cell extraction, however, makes it challenging for profiling tissues such as adipose tissue where collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nuclei extraction is often much more efficient and therefore single-nuclei RNA-sequencing (snRNA-seq) presents an alternative to scRNA-seq. However, nuclear transcripts represent only a fraction of the transcriptome in a single cell, with snRNA-seq marked with inherent transcript enrichment and detection biases. Therefore, snRNA-seq may be inadequate for mapping important transcriptional signatures in adipose tissue. In this study, we compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. We demonstrate that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, we also explore how and why the nuclear transcriptome is biased and limited, and how it can be advantageous. We robustly characterize the enrichment of nuclear-localized transcripts and adipogenic regulatory lncRNAs in snRNA-seq, while also providing a detailed understanding for the preferential detection of long genes upon using this technique. To remove such technical detection biases, we propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, we demonstrate successful integration of scRNA-seq and snRNA-seq datasets with existing bioinformatic tools. Overall, our results illustrate the applicability of snRNA-seq for characterization of cellular diversity in the adipose tissue.

bioengineering↗

bioRxiv2020

Paper-Thin Multilayer Microfluidic Devices with Integrated Valves

Integrated valve microfluidics has an unparalleled capability to automate the rapid delivery of fluids at the nanoliter scale for high-throughput biological experimentation. However, multilayer soft lithography, which is used to fabricate valve-microfluidics, produces devices with a minimum thickness of around five millimeters. This form-factor limitation prevents the use of such devices in experiments with limited sample thickness tolerance such as 4-pi microscopy, stimulated Raman scattering microscopy, and many forms of optical or magnetic tweezer applications. We present a new generation of integrated valve microfluidic devices that are less than 300 m thick, including the cover-glass substrate, that resolves the thickness limitation. This "thin-chip" was fabricated through a novel soft-lithography technique that produces on-chip micro-valves with the same functionality and reliability of traditional thick valve-microfluidic devices despite the orders of magnitude reduction in thickness. We demonstrated the advantage of using our thin-chip over traditional thick devices to automate fluid control while imaging on a high-resolution inverted microscope. First, we demonstrate that the thin-chip provides improved signal to noise when imaging single cells with two-color stimulated Raman scattering (SRS). We then demonstrated how the thin-chip can be used to simultaneously perform on-chip magnetic manipulation of beads and fluorescent imaging. This study reveals the potential of our thin-chip in high-resolution imaging, sorting, and bead capture-based single-cell multi-omics applications.

bioengineering↗

bioRxiv2019

A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) combines unbiased single-cell transcriptome measurements with surface protein quantification comparable to flow cytometry, the gold standard for cell type identification. However, current analysis pipelines cannot address the two primary challenges of CITE-seq data: combining both modalities in a shared latent space that harnesses the power of the paired measurements, and handling the technical artifacts of the protein measurement, which is obscured by non-negligible background noise. Here we present Total Variational Inference (totalVI), a fully probabilistic end-to-end framework for normalizing and analyzing CITE-seq data, based on a hierarchical Bayesian model. In totalVI, the mRNA and protein measurements for each cell are generated from a low-dimensional latent random variable unique to that cell, representing its cellular state. totalVI uses deep neural networks to specify conditional distributions. By leveraging advances in stochastic variational inference, it scales easily to millions of cells. Explicit modeling of nuisance factors enables totalVI to produce denoised data in both domains, as well as a batch-corrected latent representation of cells for downstream analysis tasks.

bioinformatics↗

bioRxiv2019

μDamID: a microfluidic approach for imaging and sequencing protein-DNA interactions in single cells

Genome regulation depends on carefully programmed protein-DNA interactions that maintain or alter gene expression states, often by influencing chromatin organization. Most studies of these interactions to date have relied on bulk methods, which in many systems cannot capture the dynamic single-cell nature of these interactions as they modulate cell states. One method allowing for sensitive single-cell mapping of protein-DNA interactions is DNA adenine methyltransferase identification (DamID), which records a proteins DNA-binding history by methylating adenine bases in its vicinity, then selectively amplifies and sequences these methylated regions. These interaction sites can also be visualized using fluorescent proteins that bind to methyladenines. Here we combine these imaging and sequencing technologies in an integrated microfluidic platform (DamID) that enables single-cell isolation, imaging, and sorting, followed by DamID. We apply this system to generate paired single-cell imaging and sequencing data from a human cell line, in which we map and validate interactions between DNA and nuclear lamina proteins, providing a measure of 3D chromatin organization and broad gene regulation patterns. DamID provides the unique ability to compare paired imaging and sequencing data for each cell and between cells, enabling the joint analysis of the nuclear localization, sequence identity, and variability of protein-DNA interactions.

bioengineering↗