Biology subjects

McCord, R. P.

Publications and source records attributed to McCord, R. P..

32 records · Page 2Linked to original sources

bioRxiv2021

Characterizing the variation in chromosome structure ensembles in the context of the nuclear microenvironment

Inside the nucleus, chromosomes are subjected to direct physical interaction between different components, active forces, and thermal noise, leading to the formation of an ensemble of three-dimensional structures. However, it is still not well understood to what extent and how the structural ensemble varies from one chromosome region or cell-type to another. We designed a statistical analysis technique and applied it to single-cell chromosome imaging data to reveal the heterogeneity of individual chromosome structures. By analyzing the resulting structural landscape, we find that the largest dynamic variation is the overall radius of gyration of the chromatin region, followed by domain reorganization within the region. By comparing different human cell-lines and experimental perturbation data using this statistical analysis technique and a network-based similarity quantification approach, we identify both cell-type and condition-specific features of the structural landscapes. We identify a relationship between epigenetic state and the properties of chromosome structure fluctuation and validate this relationship through polymer simulations. Overall, our study suggests that the types of variation in a chromosome structure ensemble are cell-type as well as region-specific and can be attributed to constraints placed on the structure by factors such as variation in epigenetic state.

genomics↗

bioRxiv2021

Enhancing Quantitative and Data Science Education for Graduate Students in Biomedical Science

Substantial guidance is available on undergraduate quantitative training for biologists, including reports focused on biomedical science, but far less attention has been paid to the graduate curriculum. In this setting, we propose an innovative approach to quantitative education that goes beyond recommendations of a course or set of courses or activities. Due to the diversity of quantitative methods, it is infeasible to expect that biomedical PhD students can be exposed to more than a minority of the quantitative concepts and techniques employed in modern biology. We developed a novel prioritization approach in which we mined and analyzed quantitative concepts and skills from publications that faculty in relevant units deemed central to the scientific comprehension of their field. The analysis provides a prioritization of quantitative skills and concepts and could represent an effective method to drive curricular focus based upon program-specific faculty input for biological science programs of all types. Our results highlight the disconnect between typical undergraduate quantitative education for life science students, focused on continuous mathematics, and the concepts and skills in graphics, statistics, and discrete mathematics that arise from priorities established by biomedical science faculty. One Sentence SummaryWe developed a novel approach to prioritize quantitative concepts and methods for inclusion in a graduate biomedical science curriculum based upon approaches included in faculty-identified key publications.

scientific communication and education↗

bioRxiv2021

sciCAN: Single-cell chromatin accessibility and gene expression data integration via Cycle-consistent Adversarial Network

The booming single-cell technologies bring a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge and gains more and more attention within the community. Here, we present a novel adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 3 state-of-the-art (SOTA) methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with better balance of mutual transferring between modalities than the other 3 SOTA methods. We further applied sciCAN to 10X Multiome data and confirmed the integrated representation preserves information of the hematopoietic hierarchy. Finally, we investigated CRSIPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.

bioinformatics↗

bioRxiv2021

Loops, TADs, Compartments, and Territories are Elastic and Robust to Dramatic Nuclear Volume Swelling

Layers of genome organization are becoming increasingly better characterized, but less is known about how these structures respond to perturbation or shape changes. Low-salt swelling of isolated chromatin fibers or nuclei has been used for decades to investigate the structural properties of chromatin. But, visible changes in chromatin appearance have not been linked to known building blocks of genome structure or features along the genome sequence. We combine low-salt swelling of isolated nuclei with genome-wide chromosome conformation capture (Hi-C) and imaging approaches to probe the effects of chromatin extension genome-wide. Photoconverted patterns on nuclei during expansion and contraction indicate that global genome structure is preserved after dramatic nuclear volume swelling, suggesting a highly elastic chromosome topology. Hi-C experiments before, during, and after nuclear swelling show changes in average contact probabilities at short length scales, reflecting the extension of the local chromatin fiber. But, surprisingly, during this large increase in nuclear volume, there is a striking maintenance of loops, TADs, active and inactive compartments, and chromosome territories. Subtle differences after expansion are observed, suggesting that the local chromatin state, protein interactions, and location in the nucleus can affect how strongly a given structure is maintained under stress. From these observations, we propose that genome topology is robust to extension of the chromatin fiber and isotropic shape change, and that this elasticity may be beneficial in physiological circumstances of changes in nuclear size and volume.

genomics↗

bioRxiv2021

Alterations in chromosome spatial compartmentalization classify prostate cancer progression

Prostate cancer aggressiveness and metastatic potential are influenced by gene expression, genomic aberrations, and cellular morphology. These processes are in turn dependent in part on the 3D structure of chromosomes, packaged inside the nucleus. Using chromosome conformation capture (Hi-C), we conducted a systematic genome architecture comparison on a cohort of cell lines that model prostate cancer progression, ranging from normal epithelium to bone metastasis. Here, we describe how chromatin compartmentalization identity (A-open vs. B-closed) changes with progression: specifically, we find that 48 gene clusters switch from the B to the A compartment, including androgen receptor, WNT5A, and CDK14. These switches could prelude transcription activation and are accompanied by changes in the structure, size, and boundaries of the topologically associating domains (TADs). Further, compartmentalization changes in chromosome 21 are exacerbated with progression and may explain, in part, the genesis of the TMPRSS2-ERG translocation: one of the main drivers of prostate cancer. These results suggest that discrete, 3D genome structure changes play a deleterious role in prostate cancer progression. SummaryThrough a systematic analysis of chromosome conformation capture in a cohort of cells that model cancer progression, San Martin et.al. find that rearrangement of the 3D genome structure in prostate cancer is a potential mechanism for disease exacerbation and that genome-wide compartment identity can classify cancer according to progression.

cancer biology↗

bioRxiv2021

SMILE: Mutual Information Learning for Integration of Single Cell Omics Data

Deep learning approaches have empowered single-cell omics data analysis in many ways, generating new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present a deep clustering algorithm that learns discriminative representation for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the same representation space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using singlecell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. SMILE works well even when feature types are unmatched, such as genes for RNA-seq and genome wide peaks for ATAC-seq.

bioinformatics↗

bioRxiv2021

CoSTA: Unsupervised Convolutional Neural Network Learning for Spatial Transcriptomics Analysis

The rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Here, we present CoSTA: a novel approach to learn spatial similarities between gene expression matrices via convolutional neural network (ConvNet) clustering. By analyzing simulated and previously published spatial transcriptomics data, we demonstrate that CoSTA learns spatial relationships between genes in a way that emphasizes whole patterns rather than pixel-level correlation. CoSTA provides a quantitative measure of how similar each pair of genes are by their spatial pattern rather than only classifying genes into categories. We find that CoSTA identifies narrower, but biologically relevant, sets of significantly related genes as compared to other approaches.

bioinformatics↗

bioRxiv2019

Inferring Chromosome Radial Organization from Hi-CData

BackgroundThe nonrandom radial organization of eukaryotic chromosome territories (CTs) inside the nucleus plays an important role in nuclear functional compartmentalization. Increasingly, chromosome conformation capture (Hi-C) based approaches are being used to characterize the genome structure of many cell types and conditions. Computational methods to extract 3D arrangements of CTs from this type of pairwise contact data will thus increase our ability to analyze CT organization in a wider variety of biological situations. ResultsA number of full-scale polymer models have successfully reconstructed the 3D structure of chromosome territories from Hi-C. To supplement such methods, we explore alternative, direct, and less computationally intensive approaches to capture radial CT organization from Hi-C data. We show that we can infer relative chromo-some ordering using PCA on a thresholded inter-chromosomal contact matrix. We simulate an ensemble of possible CT arrangements using a force-directed network layout algorithm and propose an approach to integrate additional chromosome properties into our predictions. Our CT radial organization predictions have a high correlation with microscopy imaging data for various cell nucleus geometries (lymphoblastoid, skin fibroblast, and breast epithelial cells), and we can capture previously documented changes in senescent and progeria cells. ConclusionsOur analysis approaches provide rapid and modular approaches to screen for alterations in CT organization across widely available Hi-C data. We demon-strate which stages of the approach can extract meaningful information, and also de-scribe limitations of pairwise contacts alone to predict absolute 3D positions.

bioinformatics↗

bioRxiv2019

Constricted migration contributes to persistent 3D genome structure changes associated with an invasive phenotype in melanoma cells

To spread from a localized tumor, metastatic cancer cells must squeeze through constrictions that cause major nuclear deformations. Since chromosome structure affects nucleus stiffness, gene regulation and DNA repair, here we investigate the relationship between 3D genome structure and constricted migration in cancer cells. Using melanoma (A375) cells, we identify phenotypic differences in cells that have undergone multiple rounds of constricted migration. These cells display a stably higher migration efficiency, elongated morphology, and differences in the distribution of Lamin A/C and heterochromatin. Hi-C experiments reveal differences in chromosome spatial compartmentalization specific to cells that have passed through constrictions and related alterations in expression of genes associated with migration and metastasis. Certain features of the 3D genome structure changes, such as a loss of B compartment interaction strength, are consistently observed after constricted migration in clonal populations of A375 cells and in MDA-MB-231 breast cancer cells. Our observations suggest that consistent types of chromosome structure changes are induced or selected by passage through constrictions and that these may epigenetically encode stable differences in gene expression and cellular migration phenotype.

systems biology↗

bioRxiv2019

Dynamic perineuronal net expression during learned maternal behavior is cortical hemisphere-specific and dependent on methyl-CpG-binding protein 2

Cortical neuronal circuits along the sensorimotor pathways are shaped by experience during critical periods of heightened plasticity in early postnatal development. After closure of critical periods, measured histologically by the formation and maintenance of extracellular matrix structures called perineuronal nets (PNNs), the adult mouse brain exhibits restricted plasticity and maturity. Mature PNNs are typically considered to be stable structures that restrict synaptic plasticity on cortical parvalbumin+ GABAergic neurons. Changes in environment (i.e. novel behavioral training) or social contexts (i.e. motherhood) are known to elicit synaptic plasticity in relevant neural circuitry. However, little is known about concomitant changes in the PNNs surrounding the cortical parvalbumin+ GABAergic neurons. Here, we show novel changes in PNN density in the primary somatosensory cortex (SS1) of adult female mice after maternal experience, using systematic microscopy analysis of a whole brain region. On average, PNNs were increased in the right barrel field and decreased in the left forelimb regions. Individual mice had left hemisphere dominance in PNN density. Using adult female mice deficient in methyl-CpG-binding protein 2 (MECP2), an epigenetic regulator involved in regulating experience-dependent plasticity, we found that MECP2 is critical for this precise and dynamic expression of PNN. Adult naive Mecp2-heterozygous females (Het) had increased PNN density in specific subregions in both hemispheres before maternal experience. The laterality in PNN expression seen in naive Het was lost after maternal experience, suggesting possible intact mechanisms for plasticity. Together, our results identify subregion and hemisphere-specific alterations in PNN expression in adult females, suggesting extracellular matrix plasticity as a possible neurobiological mechanism for adult behaviors in rodents.

neuroscience↗

bioRxiv2019

3D Genome Structure Variation Across Cell Types Captured by Integrating Multi-omics

Background3D genome structure contributes to the establishment or maintenance of cell identity in part by organizing genes into spatial active or inactive compartments. Less is known about how compartment switching occurs across different cell types. Rather than analyze individual A/B compartment switches between pairs of cell types, here, we seek to identify coordinated changes in groups of compartment-scale interactions across a spectrum of cell types.\n\nResultsTo characterize the impact of genome folding on cell identity, we integrated 35 Hi-C datasets with 125 DNase-seq, 244 RNA-seq, and 893 ChIP-seq datasets. We first find physical associations with the nuclear lamina inform the most dramatic changes in chromosome structure across cell types. By examining variations in chromosome structure, transcription, and chromatin accessibility, we further observe that certain sets of correlated chromosome structure contacts also co-vary in transcription and chromatin accessibility. Analyzing ChIP-seq signals, we find that sets of chromosome contacts that form and break in sync tend to share active or suppressive histone marks. Finally, we observe that similar principles appear to govern chromosome structure fluctuations across single cells as were found across cell types.\n\nConclusionOur results suggest that cells adapt their chromosome structures, guided by variable associations with the lamina and histone marks, to allocate up-regulatory or down-regulatory resources to certain regions and achieve transcription and chromatin accessibility variation. Our study shows E-PCA can identify the major variable interaction sets within populations of single cells, across broad categories of normal cell types, and between cancer and non-cancerous cell types.

bioinformatics↗

bioRxiv2019

Radiation-Induced DNA Damage and Repair Effects on 3D Genome Organization

The three-dimensional structure of chromosomes plays an important role in gene expression regulation and also influences the repair of radiation-induced DNA damage. Genomic aberrations that disrupt chromosome spatial domains can lead to diseases including cancer, but how the 3D genome structure responds to DNA damage is poorly understood. Here, we investigate the impact of DNA damage response and repair on 3D genome folding using Hi-C experiments on wild type cells and ataxia telangiectasia mutated (ATM) patient cells. Fibroblasts, lymphoblasts, and ATM-deficient fibroblasts were irradiated with 5 Gy X-rays and Hi-C was performed after 30 minutes, 24 hours, or 5 days after irradiation. 3D genome changes after irradiation were cell type-specific, with lymphoblastoid cells generally showing more contact changes than irradiated fibroblasts. However, all tested repair-proficient cell types exhibited an increased segregation of topologically associating domains (TADs). This TAD boundary strengthening after irradiation was not observed in ATM deficient fibroblasts and may indicate the presence of a mechanism to protect 3D genome structure integrity during DNA damage repair.

genomics↗

bioRxiv2018

Iteratively Improving Hi-C Experiments One Step at a Time

The 3D organization of eukaryotic chromosomes affects key processes such as gene expression, DNA replication, cell division, and response to DNA damage. The genome-wide chromosome conformation capture (Hi-C) approach can characterize the landscape of 3D genome organization by measuring interaction frequencies between all genomic regions. Hi-C protocol improvements and rapid advances in DNA sequencing power have made Hi-C useful to diverse biological systems, not only to elucidate the role of 3D genome structure in proper cellular function, but also to characterize genomic rearrangements, assemble new genomes, and consider chromatin interactions as potential biomarkers for diseases. Yet, the Hi-C protocol is still complex and subject to variations at numerous steps that can affect the resulting data. Thus, there is still a need for better understanding and control of factors that contribute to Hi-C experiment success and data quality. Here, we evaluate recently proposed Hi-C protocol modifications as well as often overlooked variables in sample preparation and examine their effects on Hi-C data quality. We examine artifacts that can occur during Hi-C library preparation, including microhomology-based artificial template copying and chimera formation that can add noise to the downstream data. Exploring the mechanisms underlying Hi-C artifacts pinpoints steps that should be further optimized in the future. To improve the utility of Hi-C in characterizing the 3D genome of specialized populations of cells or small samples of primary tissue, we identify steps prone to DNA loss which should be optimized to adapt Hi-C to lower cell numbers.\n\nHighlights3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point)\n\nO_LIVariability in Hi-C libraries can arise from early steps of cell preparation\nC_LIO_LIHi-C 2.0 changes to interaction capture steps also benefit 6-cutter libraries\nC_LIO_LIArtificial molecule fusions can arise during end repair and PCR, increasing noise\nC_LIO_LICommon causes of Hi-C DNA loss identified for future optimization\nC_LI

genomics↗

bioRxiv2018

Characterizing the 3D structure and dynamics of chromosomes and proteins in a common contact matrix framework

Any conformational ensemble of biopolymers, whether they are proteins or chromosomes, can be described using contact matrices derived from experimental or computational studies. One powerful approach to extract meaningful information from these contact matrices is to perform principal component analysis (PCA) on the covariance matrix of the contact data. Indeed, PCA on Hi-C chromosome contact matrices has revealed the spatial segregation of active and inactive chromatin. Separately, PCA on contact matrices from snapshots of protein conformations has characterized correlated fluctuations of protein domains. However, despite the similarities of these data and analyses, there has been little synergy between the PCA approaches and the comparison of resulting biological insights obtained for protein and chromosome structures. We note that, to date, different styles of analyses were applied exclusively to each biomolecule type: explicit contact correlation analysis (E-PCA) for proteins and implicit contact correlation (I-PCA) for chromosomes. In this work, we compare the results of applying both methods to both classes of biopolymers. While I-PCA reveals only average features of 3D chromosome structure, we find that applying E-PCA to an ensemble of chromosome structures from microscopy data reveals the dominant motion (concerted fluctuation) of the chromosome. Applying E-PCA to Hi-C data across the human blood cell lineage isolates the aspects of chromosome structure that most strongly differentiate cell types. Conversely, when we apply I-PCA to simulation snapshots of two protein complexes, the major component reports the consensus features of the structure, while the previously applied E-PCA characterizes correlated deviations from the mean structure.

biophysics↗