Biology subjects

Ramkumar, P.

Publications and source records attributed to Ramkumar, P..

10 recordsLinked to original sources

bioRxiv2023

Cell specificity of adeno-associated virus (AAV) serotypes in human cortical organoids

Human-derived cortical organoids (hCOs) recapitulate cell diversity and 3D structure found in the human brain and offer a promising model for discovery of new gene therapies targeting neurological disorders. Adeno-associated viruses (AAVs) are the most promising vehicles for non-invasive gene delivery to the central nervous system (CNS), but reliable and reproducible in vitro models to assess their clinical potential are lacking. hCOs can take on these issues as they are a physiologically relevant model to assess AAV transduction efficiency, cellular tropism, and biodistribution within the tissue parenchyma, all of which could significantly modulate therapeutic efficacy. Here, we examine a variety of naturally occurring AAV serotypes and measure their ability to transduce neurons and glia in hCOs from multiple donors. We demonstrate cell tropism driven by AAV serotype and hCO donor and quantify fractions of neurons and astrocytes transduced with GFP as well as overall hCO health.

neuroscience↗

bioRxiv2023

Neuroimmune cortical organoids overexpressing C4A exhibit multiple schizophrenia endophenotypes

Elevated expression of the complement component 4A (C4A) protein has been linked to an increased risk of schizophrenia (SCZ). However, there are few human models available to study the mechanisms by which C4A contributes to the development of SCZ. In this study, we established a C4A overexpressing neuroimmune cortical organoid (NICO) model, which includes mature neuronal cells, astrocytes, and functional microglia. The C4A NICO model recapitulated several neuroimmune endophenotypes observed in SCZ patients, including modulation of inflammatory genes and increased cytokine secretion. C4A expression also increased microglia-mediated synaptic uptake in the NICO model, supporting the hypothesis that synapse and brain volume loss in SCZ patients may be due to excessive microglial pruning. Our results highlight the role of C4A in the immunogenetic risk factors for SCZ and provide a human model for phenotypic discovery and validation of immunomodulating therapies.

neuroscience↗

bioRxiv2021

Functional multi-omics reveals genetic and pharmacologic regulation of surface CD38 in multiple myeloma

CD38 is a surface ectoenzyme expressed at high levels on myeloma plasma cells and is the target for the monoclonal antibodies (mAbs) daratumumab and isatuximab. Pre-treatment CD38 density on tumor cells is an important determinant of mAb efficacy. Several small molecules have been found to increase tumor surface CD38, with the goal of boosting mAb efficacy in a co-treatment strategy. Numerous other CD38-targeting therapeutics are currently in preclinical or clinical development. Here we sought to extend our currently limited insight into CD38 surface expression by using a multi-omics approach. Genome-wide CRISPR-interference screens integrated with patient-centered epigenetic analysis confirmed known regulators of CD38, such as RARA, while revealing XBP1 and SPI1 as other key transcription factors governing surface CD38 levels. CD38 knockdown followed by cell surface proteomics demonstrated no significant remodeling of the myeloma "surfaceome" after genetically-induced loss of this antigen. Integrated transcriptome and surface proteome data confirmed high specificity of all-trans retinoic acid in upregulating CD38 in contrast to broader effects of azacytidine and panobinostat. Finally, unbiased phosphoproteomics identified inhibition of MAP kinase pathway signaling in tumor cells after daratumumab treatment. Our work provides a resource to design strategies to enhance efficacy of CD38-targeting immunotherapies in myeloma.

cancer biology↗

bioRxiv2021

Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

bioinformatics↗

bioRxiv2021

Hierarchical confounder discovery in the experiment-machine learning cycle

The promise of using machine learning (ML) to extract scientific insights from high dimensional datasets is tempered by the frequent presence of confounding variables, and it behooves scientists to determine whether or not a model has extracted the desired information or instead may have fallen prey to bias. Due both to features of many natural phenomena and to practical constraints of experimental design, complex bioscience datasets tend to be organized in nested hierarchies which can obfuscate the origin of a confounding effect and obviate traditional methods of confounder amelioration. We propose a simple non-parametric statistical method called the Rank-to-Group (RTG) score that can identify hierarchical confounder effects in raw data and ML-derived data embeddings. We show that RTG scores correctly assign the effects of hierarchical confounders in cases where linear methods such as regression fail. In a large public biomedical image dataset, we discover unreported effects of experimental design. We then use RTG scores to discover cross-modal correlated variability in a complex multi-phenotypic biological dataset. This approach should be of general use in experiment-analysis cycles and to ensure confounder robustness in ML models.

bioinformatics↗

bioRxiv2020

Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease

Cerebral organoids provide unparalleled access to human brain development in vitro. However, variability induced by current culture methodologies precludes using organoids as robust disease models. To address this, we developed an automated Organoid Culture and Assay (ORCA) system to support longitudinal unbiased phenotyping of organoids at scale across multiple patient lines. We then characterized organoid variability using novel machine learning methods and found that the contribution of donor, clone, and batch is significant and remarkably consistent over gene expression, morphology, and cell-type composition. Next, we performed multi-factorial protocol optimization, producing a directed forebrain protocol compatible with 96-well culture that exhibits low variability while preserving tissue complexity. Finally, we used ORCA to study tuberous sclerosis, a disease with known genetics but poorly representative animal models. For the first time, we report highly reproducible early morphological and molecular signatures of disease in heterozygous TSC+/- forebrain organoids, demonstrating the benefit of a scaled organoid system for phenotype discovery in human disease models.

neuroscience↗

bioRxiv2019

CRISPR-based screens uncover determinants of immunotherapy response and potential combination therapy strategies

Cancer cells commonly develop resistance to immunotherapy by loss of antigen expression. Combinatorial treatments that increase levels of the target antigen on the surface of cancer cells have the potential to restore efficacy to immunotherapy. Here, we use our CRISPR interference and CRISPR activation-based functional genomics platform to systematically identify pathways controlling cell-surface expression of the multiple myeloma immunotherapy antigen - B cell maturation antigen, BCMA. We discovered that pharmacological inhibition of HDAC7 and the Sec61 complex increased cell-surface BCMA, including in primary patient cells. Importantly, pharmacological Sec61 inhibition enhanced the anti-myeloma efficacy of a BCMA-targeted antibody-drug conjugate. A CRISPR interference CAR-T coculture screen enabled us to identify both antigen-dependent and -independent mechanisms controlling response of myeloma cells to BCMA-targeted CAR-T cells. Thus, our study demonstrates the potential of CRISPR screens to uncover mechanisms controlling response of cancer cells to immunotherapy and to suggest potential combination therapies. Key PointsO_LIUsing CRISPR screens, we systematically identify mechanisms increasing expression of the immunotherapy target BCMA and ADC efficacy. C_LIO_LIWe also identify antigen-independent mechanisms regulating response of cancer cells to BCMA-CAR-T cells. C_LI

cancer biology↗

bioRxiv2019

Hue tuning curves in V4 change with visual context

Neurons are often probed by presenting a set of stimuli that vary along one dimension (e.g. color) and quantifying how this stimulus property affect neural activity. An open question, in particular where higher-level areas are involved, is how much tuning measured with one stimulus set reveals about tuning to a new set. Here we ask this question by estimating tuning to hue in macaque V4 from a set of natural scenes and a set of simple color stimuli. We found that hue tuning was strong in each dataset but was not correlated across the datasets, a finding expected if neurons have strong mixed selectivity. We also show how such mixed selectivity may be useful for transmitting information about multiple dimensions of the world. Our finding suggest that tuning in higher visual areas measured with simple stimuli may thus not generalize to naturalistic stimuli. New & NoteworthyVisual cortex is often investigated by mapping neural tuning to variables selected by the researcher such as color. How much does this approach tell us a neurons general role in vision? Here we show that for strongly hue-tuned neurons in V4, estimating hue tuning from artificial stimuli does not reveal the hue tuning in the context of natural scenes. We show how models of optimal information processing suggest that such mixed selectivity maximizes information transmission.

neuroscience↗

bioRxiv2017

Population Coding Of Conditional Probability Distributions In Dorsal Premotor Cortex

Our bodies and the environment constrain our movements. For example, when our arm is fully outstretched, we cannot extend it further. More generally, the distribution of possible movements is conditioned on the state of our bodies in the environment, which is constantly changing. However, little is known about how the brain represents such distributions, and uses them in movement planning. Here, we recorded from dorsal premotor cortex (PMd) and primary motor cortex (M1) while monkeys reached to randomly placed targets. The hands position within the workspace created probability distributions of possible upcoming targets, which affected movement trajectories and latencies. PMd, but not M1, neurons had increased activity when the monkeys hand position made it likely the upcoming movement would be in the neurons preferred directions. Across the population, PMd activity represented probability distributions of individual upcoming reaches, which depended on rapidly changing information about the bodys state in the environment.

neuroscience↗

bioRxiv2017

Modern machine learning far outperforms GLMs at predicting spikes

Neuroscience has long focused on finding encoding models that effectively ask \"what predicts neural spiking?\" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a GLM. Here we compared the predictive performance of GLMs to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. In general, the modern methods (particularly XGBoost and the ensemble) produced more accurate spike predictions and were less sensitive to the preprocessing of features. This discrepancy in performance suggests that standard feature sets may often relate to neural activity in a nonlinear manner not captured by GLMs. Encoding models built with machine learning techniques, which can be largely automated, more accurately predict spikes and can offer meaningful benchmarks for simpler models.

neuroscience↗