Biology subjects

Goff, L. A.

Publications and source records attributed to Goff, L. A..

At least 19 recordsLinked to original sources

bioRxiv2024

A consensus definition for deep layer 6 excitatory neurons in mouse neocortex

To understand neocortical function, we must first define its cell types. Recent studies indicate that neurons in the deepest cortical layer play roles in mediating thalamocortical interactions and modulating brain state and are implicated in neuropsychiatric disease. However, understanding the functions of deep layer 6 (L6b) neurons has been hampered by the lack of agreed upon definitions for these cell types. We compared commonly used methods for defining L6b neurons, including molecular, transcriptional and morphological approaches as well as transgenic mouse lines, and identified a core population of L6b neurons. This population does not innervate sensory thalamus, unlike layer 6 corticothalamic neurons (L6CThNs) in more superficial layer 6. Rather, single L6b neurons project ipsilaterally between cortical areas. Although L6b neurons undergo early developmental changes, we found that their intrinsic electrophysiological properties were stable after the first postnatal week. Our results provide a consensus definition for L6b neurons, enabling comparisons across studies.

neuroscience↗

bioRxiv2024

Transcriptional profiles of murine oligodendrocyte precursor cells across the lifespan

Oligodendrocyte progenitor cells (OPCs) are highly dynamic, widely distributed glial cells of the central nervous system (CNS) that are responsible for generating myelinating oligodendrocytes during development. By also generating new oligodendrocytes in the adult CNS, OPCs allow formation of new myelin sheaths in response to environmental and behavioral changes and play a crucial role in regenerating myelin following demyelination (remyelination). However, the rates of OPC proliferation and differentiation decline dramatically with aging, which may impair homeostasis, remyelination, and adaptive myelination during learning. To determine how aging influences OPCs, we generated a novel transgenic mouse line that expresses membrane-anchored EGFP under the endogenous promoter/enhancer of Matrilin-4 (Matn4-mEGFP) and performed high-throughput single-cell RNA sequencing, providing enhanced resolution of transcriptional changes during key transitions from quiescence to proliferation and differentiation across the lifespan. Comparative analysis of OPCs isolated from mice aged 30 to 720 days, revealed that aging induces distinct inflammatory transcriptomic changes in OPCs in different states, including enhanced activation of HIF-1 and Wnt pathways. Inhibition of these pathways in acutely isolated OPCs from aged animals restored their ability to differentiate, suggesting that this enhanced signaling may contribute to the decreased regenerative potential of OPCs with aging. This Matn4-mEGFP mouse line and single-cell mRNA datasets of cortical OPCs across ages help to define the molecular changes guiding their behavior in various physiological and pathological contexts.

neuroscience↗

bioRxiv2024

Multi-sample non-negative spatial factorization

Analyzing multi-sample spatial transcriptomics data requires accounting for biological variation. We present multi-sample non-negative spatial factorization (mNSF), an alignment-free framework extending single-sample spatial factorization (NSF) to multi-sample datasets. mNSF incorporates sample-specific spatial correlation modeling and extracts low-dimensional data representations. Through simulations and real data analysis, we demonstrate mNSFs efficacy in identifying true factors, shared anatomical regions, and region-specific biological functions. mNSFs performance is comparable to alignment-based methods when alignment is feasible, while enabling analysis in scenarios where spatial alignment is unfeasible. mNSF shows promise as a robust method for analyzing spatially resolved transcriptomics data across multiple samples.

genomics↗

bioRxiv2023

Semaphorin 6A in Retinal Ganglion Cells Regulates Functional Specialization of the Inner Retina

To form functional circuits, neurons must settle in their appropriate cellular locations and then project and elaborate neurites to contact their target synaptic neuropils. Laminar organization within the vertebrate retinal inner plexiform layer (IPL) facilitates pre- and postsynaptic neurite targeting, yet, the precise mechanisms underlying establishment of functional IPL subdomains are not well understood. Here we explore mechanisms defining the compartmentalization of OFF and ON neurites generally, and OFF and ON direction-selective neurites specifically, within the developing IPL. We show that semaphorin 6A (Sema6A), a repulsive axon guidance cue, is required for delineation of OFF versus ON circuits within the IPL: in the Sema6a null IPL, the boundary between OFF and ON domains is blurred. Furthermore, Sema6A expressed by retinal ganglion cells (RGCs) directs laminar segregation of OFF and ON starburst amacrine cell (SAC) dendritic scaffolds, which themselves serve as a substrate upon which other retinal neurites elaborate. These results demonstrate for the first time that RGCs, the first neuron-type born within the retina, play an active role in functional specialization of the IPL. Retinal ganglion cell-dependent regulation of OFF and ON starburst amacrine cell dendritic scaffold segregation prevents blurring of OFF versus ON functional domains in the murine inner plexiform layer.

neuroscience↗

bioRxiv2022

Pantr2, a trans-acting lncRNA, modulates the differentiation potential of neural progenitors in vivo

Ablation of the long non-coding RNA (lncRNA) Pantr2 results in microcephaly in a knockout murine model of corticogenesis, however, the precise mechanisms used are unknown. We present evidence that Pantr2 is a trans-acting lncRNA that regulates gene expression and chromatin accessibility both in vivo and in vitro. We demonstrate that ectopic expression of Pantr2 in a neuroblastoma cell line alters gene expression under differentiating conditions, and that both loss and gain of function of Pantr2 results in changes to cell-cycle dynamics. We show that expression of both the transcription factor Nfix and the cell cycle regulator Rgcc are negatively regulated by Pantr2. Using RNA binding protein motif analysis and existing CLIP-seq data, we annotate potential HuR and QKI binding sites on Pantr2, and demonstrate that HuR does not directly bind Pantr2 using RNA immunoprecipitation assay. Finally, using Gene Ontology enrichment analysis, we identify disruption of both Notch and Wnt signaling following loss of Pantr2 expression, indicating potential Pantr2-dependent regulation of these pathways.

neuroscience↗

bioRxiv2022

Pumping the brakes on RNA velocity -- understanding and interpreting RNA velocity estimates

RNA velocity analysis of single cells promises to predict temporal dynamics from gene expression. Indeed, in many systems, it has been observed that RNA velocity produces a vector field that qualitatively reflects known features of the system. Despite this observation, the limitations of RNA velocity estimates are poorly understood. Using real data and simulations, we dissect the impact of different steps in the RNA velocity workflow on the estimated vector field. We find that the process of mapping RNA velocity estimates into a low-dimensional representation, such as those produced by UMAP, has a large impact on the result. The RNA velocity vector field strongly depends on the k-NN graph of the data. This dependence leads to significant estimator errors when the k-NN graph is not a faithful representation of the true data structure, a feature that cannot be known for most real datasets. Finally, we establish that RNA velocity estimates expression speed neither at the gene nor cellular level. We propose that RNA velocity is best considered a smoothed interpolation of the observed k-NN structure, as opposed to an extrapolation of future cellular states, and that the use of RNA velocity as a validation of latent space embedding structures is circular.

genomics↗

bioRxiv2021

Ret loss-of-function decreases enteric neural crest progenitor proliferation and restricts developmental fate potential during enteric nervous system development

The receptor tyrosine kinase gene RET plays a critical role in the fate specification of enteric neural crest-derived cells (ENCDCs) during enteric nervous system (ENS) development. Pathogenic RET loss of function (LoF) alleles are associated with Hirschsprung disease (HSCR), which is marked by aganglionosis of the gastrointestinal (GI) tract. ENCDCs invade the developing GI tract, proliferate, migrate caudally, and differentiate into all of the major ENS cell types. Although the major phenotypic consequences and the underlying transcriptional changes from Ret LoF in the developing ENS have been described, its cell type and state-specific effects are unknown. Consequently, we performed single- cell RNA sequencing (scRNA-seq) on an enriched population of ENCDCs isolated from the developing GI tract of Ret null heterozygous and homozygous mouse embryos at embryonic day (E)12.5 and E14.5. We demonstrate four significant findings: (1) Ret-expressing ENCDCs are a heterogeneous population composed of ENS progenitors as well as glial and neuronal committed cells; (2) neurons committed to a predominantly inhibitory motor neuron developmental trajectory are not produced under Ret LoF, leaving behind a mostly excitatory motor neuron developmental program; (3) HSCR-associated and Ret gene regulatory network genes exhibit distinct expression patterns across Ret-expressing ENCDC with their expression impacted by Ret LoF; and (4) Ret deficiency leads to precocious differentiation and reduction in the number of proliferating ENS precursors. Our results support a model in which Ret contributes to multiple distinct cellular phenotypes associated with the proper development of the ENS, including the specification of inhibitory neuron subtypes, cell cycle dynamics of ENS progenitors, and the developmental timing of neuronal and glial commitment. Summary StatementRet LoF affects proper development of the mouse ENS through multiple distinct cellular phenotypes including restriction of neuronal fate potential, disruption of ENCDC migration, and modulation of progenitor proliferation rate.

developmental biology↗

bioRxiv2021

Prenatal immune stress induces a prolonged blunting of microglia activation that impacts striatal connectivity

Recent studies suggested that microglia, the primary brain immune cells, can affect circuit connectivity and neuronal function1-3. Microglia infiltrate the neuroepithelium early in embryonic development and are maintained in the brain throughout adulthood4,5. Several maternal environmental factors, such as aberrant microbiome, immune activation, and poor nutrition, can influence prenatal brain development6-8. Nevertheless, it is unknown how changes in the prenatal environment instruct the developmental trajectory of infiltrating microglia, which in turn affect brain development and function. Here we show that after maternal immune activation (MIA) microglia from the offspring have a long-lived decrease in immune reactivity (blunting) across the developmental trajectory. The blunted immune response was concomitant with changes in the chromatin accessibility and reduced transcription factor occupancy of the open chromatin. Single cell RNA sequencing revealed that MIA does not induce a distinct subpopulation but rather decreases the contribution to inflammatory microglia states. Prenatal replacement of MIA microglia with physiological infiltration of naive microglia ameliorated the immune blunting and restored a decrease in presynaptic vesicle release probability onto dopamine receptor type-two medium spiny neurons, indicating that aberrantly formed microglia due to an adverse prenatal environment impacts the long-term microglia reactivity and proper striatal circuit development.

neuroscience↗

bioRxiv2021

Identifying Gene-wise Differences in Latent Space Projections Across Cell Types and Species in Single Cell Data using scProject

Latent space techniques have emerged as powerful tools to identify genes and gene sets responsible for cell-type and species-specific differences in single-cell data. Transfer learning methods can compare learned latent spaces across biological systems. However, the robustness that comes from leveraging information across multiple genes in transfer learning is often attained at the sacrifice of gene-wise precision. Thus, methods are needed to identify genes, defined as important within a particular latent space, that significantly differ between contexts. To address this challenge, we have developed a new framework, scProject, and a new metric, projectionDrivers, to quantitatively examine latent space usage across single-cell experimental systems while concurrently extracting the genes driving the differential usage of the latent space between defined contrasts. Here, we demonstrate the efficacy, utility, and scalability of scProject with projectionDrivers and provide experimental validation for predicted species-specific differences between the developing mouse and human retina.

bioinformatics↗

bioRxiv2021

Universal prediction of cell cycle position using transfer learning

BackgroundThe cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. ResultsHere, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell cycle embedding using a fixed reference dataset and project new data into this reference embedding; an approach that overcomes key limitations of learning a dataset dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species and even sequencing assays. ConclusionsTricycle generalizes across datasets, is highly scalable and applicable to atlas-level single-cell RNA-seq data.

genomics↗

bioRxiv2019

projectR: An R/Bioconductor package for transfer learning via PCA, NMF, correlation, and clustering

MotivationDimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically import to large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically-driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset.\n\nResultsWe developed an R/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation, and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis.\n\nAvailabilityprojectR is available on Bioconductor and at https://github.com/genesofeve/projectR.\n\nContactgsteinobrien@jhmi.edu; ejfertig@jhmi.edu

bioinformatics↗

bioRxiv2018

Transcriptional suppression from KMT2D loss disrupts cell cycle and hypoxic responses in neurodevelopmental models of Kabuki syndrome

Chromatin modifiers act to coordinate gene expression changes critical to neuronal differentiation from neural stem/progenitor cells (NSPCs). Lysine-specific methyltransferase 2D (KMT2D) encodes a histone methyltransferase that promotes transcriptional activation, and is frequently mutated in cancers and in the majority (>70%) of patients diagnosed with the congenital, multisystem intellectual disability (ID) disorder Kabuki syndrome 1 (KS1). Critical roles for KMT2D are established in various non-neural tissues, but the effects of KMT2D loss in brain cell development have not been described. We conducted parallel studies of proliferation, differentiation, transcription, and chromatin profiling in KMT2D-deficient human and mouse models to define KMT2D-regulated functions in neurodevelopmental contexts, including adult-born hippocampal NSPCs in vivo and in vitro. We report cell-autonomous defects in proliferation, cell cycle, and survival, accompanied by early NSPC maturation in several KMT2D-deficient model systems. Transcriptional suppression in KMT2D-deficient cells indicated strong perturbation of hypoxia-responsive metabolism pathways. Functional experiments confirmed abnormalities of cellular hypoxia responses in KMT2D-deficient neural cells, and accelerated NSPC maturation in vivo. Together, our findings support a model in which loss of KMT2D function suppresses expression of oxygen-responsive gene programs important to neural progenitor maintenance, resulting in precocious neuronal differentiation in a mouse model of KS1.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC=\"FIGDIR/small/484410v3_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (22K):\norg.highwire.dtl.DTLVardef@55dd1aorg.highwire.dtl.DTLVardef@1270c80org.highwire.dtl.DTLVardef@a5bda2org.highwire.dtl.DTLVardef@144df37_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2018

Expression variation analysis for tumor heterogeneity in single-cell RNA-sequencing data

Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies reveal the prevalence of intra-and inter-tumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intra-and inter-tumor molecular heterogeneity. We devised a new algorithm, Expression Variation Analysis in Single Cells (EVAsc), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVAsc has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. We then apply EVAsc to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics, i.e. immunogenicity, cancer subtypes, and metastasis. Immune pathway heterogeneity in hematopoietic cell populations in breast tumors corresponded to the amount diversity present in the T-cell repertoire of each individual. In head and neck squamous cell carcinoma (HNSCC) patients, we found dramatic differences in pathway dysregulation across basal primary tumors. Within the basal primary tumors we also identified increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. Moreover, cells in HNSCC primary tumors had significantly more heterogeneity across pathways than cells in metastases, consistent with a model of clonal outgrowth. These results demonstrate the broad utility of EVAsc to quantify inter-and intra-tumor heterogeneity from scRNA-seq data without reliance on low dimensional visualization.

bioinformatics↗

bioRxiv2018

Testing the Regulatory Consequences of 1,049 Schizophrenia Associated Variants With a Massively Parallel Reporter Assay

Recent genome-wide association studies (GWAS) identified numerous schizophrenia (SZ) and Alzheimers disease (AD) associated loci, most outside protein-coding regions and hypothesized to affect gene transcription. We used a massively parallel reporter assay (MPRA) to screen, 1,049 SZ and 30 AD variants in 64 and 9 loci respectively for allele differences in driving reporter gene expression. A library of synthetic oligonucleotides assaying each allele 5 times was transfected into K562 chronic myelogenous leukemia lymphoblasts and SK-SY5Y human neuroblastoma cells. 148 variants showed allelic differences in K562 and 53 in SK-SY5Y cells, on average 2.6 variants per locus. Nine showed significant differences in both lines, a modest overlap reflecting different regulatory landscapes of these lines that also differ significantly in chromatin marks. Eight of nine were in the same direction. We observe no preference for risk alleles to increase or decrease expression. We find a positive correlation between the number of SNPs in Linkage Disequilibrium (LD) and the proportion of functional SNPs supporting combinatorial effects that may lead to haplotype selection. Our results prioritize future functional follow up of disease associated SNPs to determine the driver GWAS variant(s), at each locus and enhance our understanding of gene regulation dynamics.

genetics↗

bioRxiv2018

Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species.

New approaches are urgently needed to glean biological insights from the vast amounts of single cell RNA sequencing (scRNA-Seq) data now being generated. To this end, we propose that cell identity should map to a reduced set of factors which will describe both exclusive and shared biology of individual cells, and that the dimensions which contain these factors reflect biologically meaningful relationships across different platforms, tissues and species. To find a robust set of dependent factors in large-scale scRNA- Seq data, we developed a Bayesian non-negative matrix factorization (NMF) algorithm, scCoGAPS. Application of scCoGAPS to scRNA-Seq data obtained over the course of mouse retinal development identified gene expression signatures for factors associated with specific cell types and continuous biological processes. To test whether these signatures are shared across diverse cellular contexts, we developed projectR to map biologically disparate datasets into the factors learned by scCoGAPS. Because projecting these dimensions preserve relative distances between samples, biologically meaningful relationships/factors will stratify new data consistent with their underlying processes, allowing labels or information from one dataset to be used for annotation of the other--a machine learning concept called transfer learning. Using projectR, data from multiple datasets was used to annotate latent spaces and reveal novel parallels between developmental programs in other tissues, species and cellular assays. Using this approach we are able to transfer cell type and state designations across datasets to rapidly annotate cellular features in a new dataset without a priori knowledge of their type, identify a species-specific signature of microglial cells, and identify a previously undescribed subpopulation of neurosecretory cells within the lung. Together, these algorithms define biologically meaningful dimensions of cellular identity, state, and trajectories that persist across technologies, molecular features, and species.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC=\"FIGDIR/small/395004_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (81K):\norg.highwire.dtl.DTLVardef@dd1c07org.highwire.dtl.DTLVardef@5b1109org.highwire.dtl.DTLVardef@bb6714org.highwire.dtl.DTLVardef@16c66f0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2017

Enter the matrix: Interpreting unsupervised feature learning with matrix decomposition to discover hidden knowledge in high-throughput omics data

Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge--answering questions from high-dimensional data that we have not yet thought to ask.

systems biology↗

bioRxiv2017

Linear models enable powerful differential activity analysis in massively parallel reporter assays

Massively parallel reporter assays (MPRAs) have emerged as a popular means for understanding noncoding variation in a variety of conditions. While a large number of experiments have been described in the literature, analysis typically uses ad-hoc methods. There has been little attention to comparing performance of methods across datasets. We present the mpralm method which we show is calibrated and powerful, by analyzing its performance on multiple MPRA datasets. We show that it outperforms existing statistical methods for analysis of this data type, in the first comprehensive evaluation of statistical methods on several datasets. We investigate theoretical and real-data properties of barcode summarization methods and show an unappreciated impact of summarization method for some datasets. Finally, we use our model to conduct a power analysis for this assay and show substantial improvements in power by performing up to 6 replicates per condition, whereas sequencing depth has smaller impact; we recommend to always use at least 4 replicates. Together, these results inform recommendations for differential analysis, general group comparisons, and power analysis and will help improve design and analysis of MPRA experiments. An R package is available from the Bioconductor project at https://bioconductor.org/packages/mpra.

bioinformatics↗

bioRxiv2017

Variation in neuronal activity state, axonal projection target, and position principally define the transcriptional identity of individual neocortical projection neurons.

Single-cell RNA sequencing technologies have generated the first catalogs of transcriptionally defined neuronal subtypes of the brain. However, the biologically informative cellular processes that contribute to neuronal subtype specification and transcriptional heterogeneity remain unclear. By comparing the gene expression profiles of single layer 6 corticothalamic neurons in somatosensory cortex, we show that transcriptional subtypes primarily reflect axonal projection pattern, laminar position within the cortex, and neuronal activity state. Pseudotemporal ordering of 1023 cellular responses to manipulation of sensory input demonstrates that changes in expression of activity-induced genes both reinforced cell-type identity and contributed to increased transcriptional heterogeneity within each cell type. This is due to cell-type specific biases in the choice of transcriptional states following manipulation of neuronal activity. These results reveal that axonal projection pattern, laminar position, and activity state define significant axes of variation that contribute both to the transcriptional identity of individual neurons and to the transcriptional heterogeneity within each neuronal subtype.

neuroscience↗