bioRxiv Science⌕ Search

Biology subjects

Pachter, L.

Publications and source records attributed to Pachter, L..

At least 55 records · Page 3Linked to original sources
bioRxiv2023

Recovery of a learned behavior despite partial restoration of neuronal dynamics after chronic inactivation of inhibitory neurons

Maintaining motor behaviors throughout life is crucial for an individuals survival and reproductive success. The neuronal mechanisms that preserve behavior are poorly understood. To address this question, we focused on the zebra finch, a bird that produces a highly stereotypical song after learning it as a juvenile. Using cell-specific viral vectors, we chronically silenced inhibitory neurons in the pre-motor song nucleus called the high vocal center (HVC), which caused drastic song degradation. However, after producing severely degraded vocalizations for around 2 months, the song rapidly improved, and animals could sing songs that highly resembled the original. In adult birds, single-cell RNA sequencing of HVC revealed that silencing interneurons elevated markers for microglia and increased expression of the Major Histocompatibility Complex I (MHC I), mirroring changes observed in juveniles during song learning. Interestingly, adults could restore their songs despite lesioning the lateral magnocellular nucleus of the anterior neostriatum (LMAN), a brain nucleus crucial for juvenile song learning. This suggests that while molecular mechanisms may overlap, adults utilize different neuronal mechanisms for song recovery. Chronic and acute electrophysiological recordings within HVC and its downstream target, the robust nucleus of the archistriatum (RA), revealed that neuronal activity in the circuit permanently altered with higher spontaneous firing in RA and lower in HVC compared to control even after the song had fully recovered. Together, our findings show that a complex learned behavior can recover despite extended periods of perturbed behavior and permanently altered neuronal dynamics. These results show that loss of inhibitory tone can be compensated for by recovery mechanisms partly local to the perturbed nucleus and do not require circuits necessary for learning.

neuroscience↗
bioRxiv2023

Studying stochastic systems biology of the cell with single-cell genomics data

Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.

biophysics↗
bioRxiv2023

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity

The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3 end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3 processing are deployed across human tissues, with nearly half of multitranscript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

genomics↗
bioRxiv2023

Direct androgen receptor regulation of sexually dimorphic gene expression in the mammalian kidney

Mammalian organs exhibit distinct physiology, disease susceptibility and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA-seq data demonstrated sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR) mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation while analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, and disease and metabolic linkage, of sexually dimorphic gene activity.

developmental biology↗
bioRxiv2023

Flexible parsing and preprocessing of technical sequences with splitcode

Next-generation sequencing libraries are constructed with numerous synthetic constructs such as sequencing adapters, barcodes, and unique molecular identifiers. Such sequences can be essential for interpreting results of sequencing assays, and when they contain information pertinent to an experiment, they must be processed and analyzed. We present a tool called splitcode, that enables flexible and efficient parsing, interpreting, and editing of sequencing reads. This versatile tool facilitates simple, reproducible preprocessing of reads from libraries constructed for a large array of single-cell and bulk sequencing assays. Availability and ImplementationThe splitcode program is free, open source, and available for download at http://github.com/pachterlab/splitcode.

bioinformatics↗
bioRxiv2023

A machine-readable specification for genomics assays

Understanding the structure of sequenced fragments from genomics libraries is essential for accurate read preprocessing. Currently, different assays and sequencing technologies require custom scripts and programs that do not leverage the common structure of sequence elements present in genomics libraries. We present seqspec, a machine-readable specification for libraries produced by genomics assays that facilitates standardization of preprocessing and enables tracking and comparison of genomics assays. The specification and associated seqspec command line tool is available at https://github.com/IGVF/seqspec.

bioinformatics↗
bioRxiv2023

The telegraph process is not a subordinator

Investigations of transcriptional models by Amrhein et al. outline a strategy for connecting steady-state distributions to process dynamics. We clarify its limitations: the strategy holds for a very narrow class of processes, which excludes an example given by the authors.

biophysics↗
bioRxiv2023

Mechanistic modeling with a variational autoencoder for multimodal single-cell RNA sequencing data

We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, biVI models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that biVI captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions. On biological data, biVI provides a scalable route for identifying the biophysical mechanisms underlying gene expression. This analytical approach outlines a generalizable strateg for treating multimodal datasets generated by high-throughput, single-cell genomic assays.

biophysics↗
bioRxiv2022

Cell-specific occupancy dynamics between the pioneer-like factor Opa/ZIC and Ocelliles/OTX regulate early head development in embryos

During development, embryonic patterning systems direct a set of initially uncommitted pluripotent cells to differentiate into a variety of cell types and tissues. A core network of transcription factors, such as Zelda/POU5F1, Odd-paired (Opa)/ZIC and Ocelliless (Oc)/OTX, are conserved across animals. While Opa is essential for a second wave of zygotic activation after Zelda, it is unclear whether Opa drives head cell specification past gastrulation onset, in the Drosophila embryo. Our hypothesis is that Opa and Oc are interacting with distinct cis-regulatory regions for shaping cell fates in the embryonic head. Using super-resolution microscopy and epigenomic meta-analysis of single cell RNAseq datasets we find that opas and ocs overlapping expression domains are dynamic in the head region, with both factors being simultaneously transcribed at the blastula stage. However, analysis of single-embryo RNAseq data reveals a subgroup of Opa-bound genes to be Opa-independent in the cellularized embryo. Interrogation of these genes against Oc ChIPseq combined with in situ data, suggests that Opa is competing with Oc for the regulation of a subgroup of genes later in gastrulation. Specifically, we find that Oc binds to late, head-specific enhancers independently and activates them in a head-specific wave of zygotic transcription, suggesting distinct roles for Oc in the blastula and gastrula stages.

developmental biology↗
bioRxiv2022

Annotation-agnostic discovery of associations between novel gene isoforms and phenotypes

We present a novel method for associating phenotypes with RNA expression, that can identify expression associations resulting from a wide variety of underlying transcriptional and post-transcriptional events, without relying on annotations of these events. We show that we can reliably detect, de novo, phenotypically relevant transcriptional structures

bioinformatics↗
bioRxiv2022

Accurate quantification of single-nucleus and single-cell RNA-seq transcripts

In single-cell and single-nucleus RNA sequencing, the coexistence of nascent (unprocessed) and mature (processed) mRNA poses challenges in accurate read mapping and the interpretation of count matrices. The traditional transcriptome reference, defining the region of interest in bulk RNA-seq, restricts its focus to mature mRNA transcripts. This restriction leads to two problems: reads originating outside of the region of interest are prone to mismapping within this region, and additionally, such external reads cannot be matched to specific transcript targets. Expanding the region of interest to encompass both nascent and mature mRNA transcript targets provides a more comprehensive framework for RNA-seq analysis. Here, we introduce the concept of distinguishing flanking k-mers (DFKs) to improve mapping of sequencing reads. We have developed an algorithm to identify DFKs, which serve as a sophisticated background filter, enhancing the accuracy of mRNA quantification. This dual strategy of an expanded region of interest coupled with the use of DFKs enhances the precision in quantifying both mature and nascent mRNA molecules, as well as in delineating reads of ambiguous status.

bioinformatics↗
bioRxiv2022

Transient and delay chemical master equations

The serial nature of reactions involved in the RNA life-cycle motivates the incorporation of delays in models of transcriptional dynamics. The models couple a bursty or switching promoter to a fairly general set of Markovian or deterministically delayed monomolecular RNA interconversion reactions with no feedback. We provide numerical solutions for the RNA copy number distributions the models induce, and solve several systems with splicing and degradation. An analysis of single-cell and single-nucleus RNA sequencing data using these models reveals that the kinetics of nuclear export do not appear to require invocation of a non-Markovian waiting time.

biophysics↗
bioRxiv2022

Quantifying orthogonal barcodes for sequence census assays

Barcode-based sequence census assays utilize custom or random oligonucloetide sequences to label various biological features, such as cell-surface proteins or CRISPR perturbations. These assays all rely on barcode quantification, a task that is complicated by barcode design and technical noise. We introduce a modular approach to quantifying barcodes that achieves speed and memory improvements over existing tools. We also introduce a set of quality control metrics, and accompanying tool, for validating barcode designs.

bioinformatics↗
bioRxiv2022

Spectral neural approximations for models of transcriptional dynamics

The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an un-precedented, genome-wide scale. These transcriptional systems, which involve discrete, stochastic events, are naturally modeled using Chemical Master Equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life-cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, where solutions are approximated by (1) learning the weights of kernel distributions with constrained parameters, or (2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression (KWR) and parameter scaled kernel weight regression (psKWR), respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data. Statement of significanceThe life-cycles of RNA molecules are governed by a set of stochastic events that result in heterogeneous gene expression patterns in genetically identical cells, resulting in the vast diversity of cellular types, responses, and functions. While stochastic models have been used in the field of fluorescence transcriptomics to understand how cells exploit and regulate this inherent randomness, biophysical models have not been widely applied to high-throughput transcriptomic data, as solutions are often intractable and computationally impractical to scale. Our neural approximations of solutions to a two-species transcriptional system enable efficient inference of rates that drive the dynamics of gene expression, thus providing a scalable route to extracting mechanistic information from increasingly available multi-species single-cell transcriptomics data.

bioinformatics↗
bioRxiv2022

Monod: mechanistic analysis of single-cell RNA sequencing count data

Single-cell RNA sequencing analysis centers on illuminating cell diversity and understanding the transcriptional mechanisms underlying cellular function. These datasets are large, noisy, and complex. Current analyses prioritize noise removal and dimensionality reduction to tackle these challenges and extract biological insight. We propose an alternative, physical approach to leverage the stochasticity, size, and multimodal nature of these data to explicitly distinguish their biological and technical facets while revealing the underlying regulatory processes. With the Python package Monod, we demonstrate how nascent and mature RNA counts, present in most published datasets, can be meaningfully "integrated" under biophysical models of transcription. By utilizing variation in these modalities, we can identify transcriptional modulation not discernible though changes in average gene expression, quantitatively compare mechanistic hypotheses of gene regulation, analyze transcriptional data from different technologies within a common framework, and minimize the use of opaque or distortive normalization and transformation techniques.

bioinformatics↗
bioRxiv2022

Pseudoalignment facilitates assignment of error-prone Ultima Genomics reads

We analyze single-cell RNA-seq data sequenced with Ultima Genomics technology and find high error rates in and near homopolymers. To compensate for these errors, we explore the use of pseudoalignment for read assignment, and find that it can perform better than standard read alignment. Our pseudoalignment read assignment for Ultima Genomics data is available as part of the kallisto-bustools kb-python package available at https://github.com/pachterlab/kb_python.

genomics↗
bioRxiv2022

Efficient querying of genomic databases for single-cell RNA-seq with gget

MotivationA recurring challenge in interpreting genomic data is the assessment of results in the context of existing reference databases. Currently, there is no tool implementing automated, easy programmatic access to curated reference information stored in a diverse collection of large, public genomic databases. Resultsgget is a free and open-source command-line tool and Python package that enables efficient querying of genomic reference databases, such as Ensembl. gget consists of a collection of separate but interoperable modules, each designed to facilitate one type of database querying required for genomic data analysis in a single line of code. AvailabilityThe manual and source code are available at https://github.com/pachterlab/gget. Contactlpachter@caltech.edu

bioinformatics↗