Biology subjects

Raj, A.

Publications and source records attributed to Raj, A..

At least 91 records · Page 5Linked to original sources

bioRxiv2020

Connectome-mediated prediction of future tau-PET burden in Alzheimer's disease

Alzheimers Disease (AD) tau pathology originates in the brainstem and subsequently spreads to the entorhinal cortex, hippocampus and finally to temporal, parietal and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to trans-neuronal spread of misfolded tau protein along the projection pathways of affected neurons. The aggregation of tau is being increasingly recognized as a trustworthy biomarker preceding the appearance of Alzheimers disease (AD) symptoms. One major goals of disease modifying therapies has been to stop or slow down the tau aggregation process. In order to evaluate drug efficacy, it would be desirable to have an accurate model predictive of a patients future tau burden, against which the tau measurements from drug-receiving cohorts could be compared. Here we report the development of such a model, evaluated in a cohort of 88 subjects clinically diagnosed as Mild Cognitively Impaired (MCI = 60) or Alzheimers disease (AD = 28) and tracked over a period of 18 months. Our approach combined data-driven and model-based methodologies, with the goal of predicting changes in tau within suitably specified target regions. We show that traditional statistical methods, allied to a network diffusion model for tau propagation in the brain, provide a remarkable prediction of the magnitude of incremental tau deposited in particular cortical areas of the brain over this period (MCI: R2 = 0.65{+/-}0.16; AD: R2 = 0.71{+/-}0.11) from baseline data. Our work has the potential to greatly strengthen the repertoire of analysis tools used in AD clinical trials, opening the door to future interventional trials with far fewer sample sizes than currently required.

neuroscience↗

bioRxiv2020

Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity

Our ability to identify the particular transcription factors that maintain cell type is limited. Identification of factors by their cell type-specific expression or their participation in developmental regulation has been only modestly successful. We hypothesized that because cell type is often resilient to perturbations, the transcriptional response to perturbations would identify identity-maintaining factors. We developed Perturbation Panel Profiling (P3) as a framework for perturbing cells in dozens of conditions and measuring gene expression responsiveness transcriptome-wide. Applying P3 to human iPSC-derived cardiac myocytes showed that transcription factors known to function in cardiac differentiation and maintenance were among the most frequently up-regulated (most responsive). We reasoned that one potential function of responsive genes may be to maintain cellular identity. We identified responsive transcription factors in fibroblasts using P3 and found that suppressing their expression led to enhanced reprogramming efficiency. We propose that responsiveness to perturbations is a property of factors that help maintain cellular identity.

systems biology↗

bioRxiv2020

Gene regulation gravitates towards either addition or multiplication when combining the effects of two signals

Signals often ultimately affect the transcription of genes, and often, two different signals can affect the transcription of the same gene. In such cases, it is natural to ask how the combined transcriptional response compares to the individual responses. Mechanistic models can predict a range of combined responses, with the most commonly applied models predicting additive or multiplicative responses, but systematic genome-wide evaluation of these predictions are not available. Here, we performed a comprehensive analysis of the transcriptional response of human MCF-7 cells to two different signals (retinoic acid and TGF-{beta}), applied individually and in combination. We found that the combined responses exhibited a range of behaviors, but clearly favored both additive and multiplicative combined transcriptional responses. We also performed paired chromatin accessibility measurements to measure putative transcription factor occupancy at regulatory elements near these genes. We found that increases in chromatin accessibility were largely additive, meaning that the combined accessibility response was the sum of the accessibility responses to each signal individually. We found some association between super-additivity of accessibility and multiplicative or super-multiplicative combined transcriptional responses, while sub-additivity of accessibility associated with additive transcriptional responses. Our findings suggest that mechanistic models of combined transcriptional regulation must be able to reproduce a range of behaviors.

systems biology↗

bioRxiv2020

The spatial distributions of pre-mRNAs suggest post-transcriptional splicing of specific introns within endogenous genes

Splicing is the stepwise molecular process by which introns are removed from pre-mRNA and exons are joined together to form mature mRNA sequences. The ordering and spatial distribution of these steps remain controversial, with opposing models suggesting splicing occurs either during or after transcription. We used single-molecule RNA FISH, expansion microscopy, and live-cell imaging to reveal the spatiotemporal distribution of nascent transcripts in mammalian cells. At super-resolution levels, we found that pre-mRNA formed clouds around the transcription site. These clouds indicate the existence of a transcription site proximal zone through which RNA move more slowly than in the nucleoplasm. Full-length pre-mRNA undergo continuous splicing as they move through this zone following transcription, suggesting a model in which splicing can occur post-transcriptionally but still within the proximity of the transcription site, thus seeming co-transcriptional by most assays. These results may unify conflicting reports of co-transcriptional versus post-transcriptional splicing.

cell biology↗

bioRxiv2020

Retrospective identification of rare cell populations underlying drug resistance connects molecular variability with cell fate

Molecular differences between individual cells can lead to dramatic differences in cell fate, such as death versus survival of cancer cells upon drug treatment. These originating differences remain largely hidden due to difficulties in determining precisely what variable molecular features lead to which cellular fates. Thus, we developed Rewind, a methodology that combines genetic barcoding with RNA FISH to directly capture rare cells that give rise to cellular behaviors of interest. Applied to BRAFV600E melanoma, we trace drug-resistant cell fates back to single-cell gene expression differences in their drug-naive precursors (initial frequency of [~]1:1000-1:10,000 cells) and relative persistence of MAP-kinase signaling soon after drug treatment. Within this rare subpopulation, we uncover a rich substructure in which molecular differences between several distinct subpopulations predict future differences in phenotypic behavior, such as proliferative capacity of distinct resistant clones following drug treatment. Similarly, we show that treatments that modify the frequency of resistance can allow otherwise non-resistant cells in the drug-naive population to become resistant, and that these new populations are marked by the variable expression of distinct genes. Together, our results reveal the presence of hidden, rare-cell variability that can underlie a range of latent phenotypic outcomes upon drug exposure.

systems biology↗

bioRxiv2019

Social reprogramming in ants induces longevity-associated glia remodeling

Healthy brain aging is of crucial societal importance; however, the mechanisms that regulate it are largely unknown. Social insects are an outstanding model to study the impact of environment and epigenetics on brain function and aging because workers and queens arise from the same genome but display profound differences in behavior and longevity. In Harpegnathos saltator ants, adult workers can transition to a queen-like phenotypic state called gamergate. This caste transition results in reprogramming of social behavior and lifespan extension. Whether these changes in brain function and physiology cause brain remodeling at the cellular level is not known. Using single-cell RNA-sequencing of Harpegnathos brains undergoing caste transition, we uncovered shifts in neuronal and glial populations. In particular, the conversion of workers into long-lived gamergates caused the expansion of ensheathing glia, which maintain brain health by phagocytosing damaged neuronal structures. These glia cells were lost during aging in normal workers but not in longer-lived gamergates. We observed similar caste- and age-associated differences in ensheathing glia in other Hymenoptera as well as Drosophila melanogaster. We propose that enhanced glia-based neuroprotection promotes healthy brain aging and contributes to the extended lifespan of the reproductive caste in social insects.

neuroscience↗

bioRxiv2019

Matrix Inversion and Subset Selection (MISS): A novel pipeline for quantitative mapping of diverse cell types across the murine brain

The advent of increasingly sophisticated imaging platforms has allowed for the visualization of the murine nervous system at single-cell resolution. However, current experimental approaches have not yet produced whole-brain maps of a comprehensive set of neuronal and nonneuronal types that approaches the cellular diversity of the mammalian cortex. Here we aim to fill in this gap in knowledge with an open-source computational pipeline, Matrix Inversion with Subset Selection (MISS), that can infer quantitatively validated distributions of diverse collections of neural cell types at 200m resolution using a combination of single-cell RNAseq and in situ hybridization datasets. We rigorously demonstrate the accuracy of MISS against literature expectations. Importantly, we show that gene subset selection, a procedure by which we filter out low-information genes prior to performing deconvolution, is a critical pre-processing step that distinguishes MISS from its predecessors and facilitates the production of cell type maps with significantly higher accuracy. We also show that MISS is generalizable by generating high-quality cell type maps from a second, independently curated single-cell RNAseq dataset. Together, our results illustrate the viability of computational approaches for determining the spatial distributions of a wide variety of cell types from genetic data alone.

neuroscience↗

bioRxiv2019

A Maxent modelling with a geospatial approach for the Habitat suitability of Flamingos in an Evanescing Ramsar site (Sambhar Lake, India) over the changing climatic scenarios

Wetlands play a crucial role in the biosphere and provide numerous services. They performed multiple functions such as groundwater recharge, water purification, conservation of biological resources, act as a carbon sink and habitat of amphibians and birds. A Ramsar site-the Sambhar Lake is one of the largest inland saline wetland present in the arid region of Rajasthan, India has unique habitat suitability for the winter avifauna migrants like flamingoes and falcons. The occurrence of suitable climatic conditions and food availability like brine shrimps (Artemia salina) attracts flocks of migratory birds. From the last three decades, Sambhar Lake has been continuously facing degradation due to anthropogenic activities, which disturb Lakes natural ecology and existence. These cause disturbances in habitat suitability of migratory birds in the Sambhar Lake, which leads to a reduction of population density of migratory birds. Therefore, this study aimed to assess the degradation and vulnerability of Sambhar Lake and the habitat suitability of migratory birds using Maxent Habitat Suitability model. This model provides a platform to integrate the birds occurrence data with the bioclimatic variables using remote sensing and Geographical Information System, and provides birds habitat suitability as well as predicts future birds occurrence scenarios. Landsat-5 and Sentinel-2 imagery for the year 1996 and 2019 respectively were used in this study. Four indicators such as LULC NDWI, MSI and SABI depicts the environmental condition of the Sambhar Lake. Output form Maxent model reveals that the Sambhar lake area with increasing anthropogenic activities has become unsuitable for flamingos. A remarkable loss of breeding sites of animal, particularly avian fauna (flamingos) is seen in the recent years due to different types of threats posed on the Ramsar site. Increase in Salt crust and Vegetation area from 36.8055 to 123.837 Sq. Km. and 26.5347 to 36.857 Sq. Km. respectively have taken place. While a decrease in saline water area from 88.8309 to 19.3256 Sq. Km has been observed, within the vicinity of Sambhar Lake as clearly shown through LULC map. The future prediction of the distribution of species in the region for the year 2050 shows that the most suitable regions will be near to Jhapok and nearby waters of Salt Lake City as the drains from the city opens in the lake where the flamingoes get Algae in the form of food. Active steps are needed for the lake conservation to reduce the risks of migratory birds population.

ecology↗

bioRxiv2019

Gene networks with transcriptional bursting recapitulate rare transient coordinated expression states in cancer

SUMMARYNon-genetic transcriptional variability at the single-cell level is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of melanoma cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes. Importantly, these rare cells are able to survive drug treatment and develop resistance. How might these extremely rare states arise and disappear within the population? It is unclear whether the canonical stochastic models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified molecular mechanisms. Here we use mathematical modeling to show that a minimal network comprising of transcriptional bursting and interactions between genes can give rise to rare coordinated high states. We next show that although these states occur across networks of different sizes, they depend strongly on three (out of seven) model parameters and require network connectivity to be [≤] 6. Interestingly, we find that while entry into the rare coordinated high state is initiated by a long transcriptional burst that also triggers entry of other genes, the exit from it occurs through the independent inactivation of individual genes. Finally, our model predicts that increased network connectivity can lead to transcriptionally stable states, which we verify using network inference analysis of experimental data. In sum, we demonstrate that established principles of gene regulation are sufficient to describe this new class of rare cell variability and argue for its general existence in other biological contexts.

systems biology↗

bioRxiv2019

Genetic screening for single-cell variability modulators driving therapy resistance

Cellular plasticity describes the ability of cells to transition from one set of phenotypes to another. In the context of cancer therapeutics, plasticity refers to transient fluctuations in the molecular state of tumor cells, driving the formation of rare cells primed to survive drug treatment and ultimately reprogram into a stably resistant fate. However, the biological processes governing this cellular plasticity remain unknown. We used CRISPR/Cas9 genetic screens to reveal genes that affect cell fate decisions by altering cellular plasticity across a range of functional categories. We found that cellular plasticity and cell fate decision making can be decoupled in that factors can affect cell fate decisions in both plasticity-dependent and independent manners. We discovered a novel mode of altering resistance based on cellular plasticity that, contrary to known mechanisms, pushes cells towards a more differentiated state. We further confirmed our prediction that manipulating cellular plasticity before the addition of the main therapy would result in changes in therapy resistance more than concurrent administration. Together, our results indicate that identifying pathways modulating cellular plasticity has the potential to alter cell fate decisions and may provide a new avenue for treating drug resistance.

cell biology↗

bioRxiv2019

Spectral Graph Theory of Brain Oscillations

The relationship between the brains structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectomes Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled non-linear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30Hz) activity estimated from source localized scalp magneto-encephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. Significance StatementThe relationship between the brains structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectomes Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics.

neuroscience↗

bioRxiv2018

Identifiability in connectome based neural mass models

The spatial-temporal patterns of neuronal dynamics emerge from the network of coordinated brain regions, this structure-function relationship of the brain can be described mathematically by biophysical models of coupled brain regions connected by white matter tractography. Implementations of such models have focused on reproducing functional connectivity extracted from functional magnetic resonance imaging (fMRI), but these efforts are limited by the temporal resolution of fMRI data and the reduction of time course recordings into phenomenological functional connectivity maps. Here, we optimize parameters of a neural mass model (NMM) to best fit region-wise power spectra across the whole brain estimated from source localized electroencephalography (EEG). NMM models with global parameters were not able to fully reproduce region-wise power spectra, with or without the inclusion of structural connectivity information. In contrast, without the inclusion of structural connectivity information, independent oscillators at each brain region are able to reproduce region-wise power spectra. But the addition of structural connectivity and transmission delays to the NMM does not improve overall power spectra fit. Connectome-based NMM implementations with regional parameters lead to high dimensional network models that produce non-unique results. Inherent parameter identifiability problem in network models poses challenges for using such models as diagnostic tools for neurological diseases.

neuroscience↗

bioRxiv2018

Heterogeneity of Incipient Atrophy Patterns in Parkinsons Disease

Parkinsons Disease (PD) is a the second most common neurodegenerative disorder after Alzheimers disease and is characterized by cell death in the amygdala and in substructures of the basal ganglia such as the substantia nigra. Since neuronal loss in PD leads to measurable atrophy patterns in the brain, there is clinical value in understanding where exactly the pathology emerges in each patient and how incipient atrophy relates to the future spread of disease. A recent seed-inference algorithm combining an established network-diffusion model with an L1-penalized optimization routine led to new insights regarding the non-stereotypical origins of Alzheimers pathologies across individual subjects. Here, we leverage the same technique to PD patients, demonstrating that the high variability in their atrophy patterns also translates into heterogeneous seed locations. Our individualized seeds are significantly more predictive of future atrophy than a single seed placed at the substantia nigra or the amygdala. We also found a clear distinction in seeding patterns between two PD subgroups - one characterized by predominant involvement of brainstem and ventral nuclei, and the other by more widespread frontal and striatal cortices. This might be indicative of two distinct etiological mechanisms operative in PD. Ultimately, our methods demonstrate that the early stages of the disease may exhibit incipient atrophy patterns that are more complex and variable than generally appreciated.

neuroscience↗

bioRxiv2018

Allele-specific RNA imaging shows that allelic imbalances can arise in tissues through transcriptional bursting

Extensive cell-to-cell variation exists even among putatively identical cells, and there is great interest in understanding how the properties of transcription relate to this heterogeneity. Differential expression from the two gene copies in diploid cells could potentially contribute, yet our ability to measure from which gene copy individual RNAs originated remains limited, particularly in the context of tissues. Here, we demonstrate quantitative, single molecule allele-specific RNA FISH adapted for use on tissue sections, allowing us to determine the chromosome of origin of individual RNA molecules in formaldehyde-fixed tissues. We used this method to visualize the allele-specific expression of Xist and multiple autosomal genes in mouse kidney. By combining these data with mathematical modeling, we evaluated models for allele-specific heterogeneity, in particular demonstrating that apparent expression from only one of the alleles in single cells can arise as a consequence of low-level mRNA abundance and transcriptional bursting.

genetics↗

bioRxiv2018

Memory sequencing reveals heritable single cell gene expression programs associated with distinct cellular behaviors

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. Yet it remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we report a method combining Luria and Delbrucks fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several cell divisions. MemorySeq revealed multiple gene modules that are expressed together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors, such as the ability to proliferate in the face of anti-cancer therapeutics, in different cancer cell lines. The identification of non-genetic, multigenerational fluctuations has the potential to reveal new forms of biological memory at the level of single cells and suggests that non-genetic heritability of cellular state may be a quantitative property.

systems biology↗

bioRxiv2018

LADL: Light-activated dynamic looping for endogenous gene expression control

Mammalian genomes are folded into tens of thousands of long-range looping interactions1,2. The cause and effect relationship between looping and genome function is poorly understood, and the extent to which chromatin loops are dynamic on short time scales remains a fundamental unanswered question. Currently available strategies for loop engineering involve synthetic transcription factors tethered to dCas93,4 or zinc fingers5,6, which are constitutively expressed5,6 or induced on long time scales by the presence of a small molecule3. Here we report a new class of 3-D optoepigenetic tools for the directed rearrangement of 3-D chromatin looping on short time scales using blue light. We create synthetic architectural proteins by fusing the CIBN protein subunit from Arabidopsis thaliana with enzymatically dead Cas9 (dCas9). We target our light-activated dynamic looping system (LADL) to two genomic anchors with CRISPR guide RNAs and engineer their spatial co-localization via light-induced heterodimerization of the cryptochrome 2 (CRY2) protein with dCas9-CIBN. We apply LADL to redirect a stretch enhancer (SE) away from its endogenous Klf4 target gene and to the Zfp462 promoter. Looping changes occur as early as four hours after light induction. Using single molecule RNA FISH, we observe a LADL-induced increase in the total nascent Zfp462 transcripts and the number of Zfp462 alleles expressing simultaneously per cell. Moreover, LADL also increased synchronous Sox2 expression after reinforcement of a known Sox2-SE looping interaction. LADL facilitates loop synchronization across a large population of cells without exogenous chemical cofactors and can enable future efforts to engineer reversible and oscillatory looping on short time scales.

molecular biology↗

bioRxiv2018

Functional rerouting via the structural connectome is associated with better recovery after mild TBI

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4{+/-}8.0 years, 20 male) at 1 and 6 months post-injury. We quantified the relationship between functional and structural connectomes using network diffusion model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t25=-2.15, p=0.04). None of the structural or functional global connectome metrics were significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6{+/-}8.8 years, 25 male). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R2 = 0.27). We observe that increased network diffusion model propagation time, increased structural connectome segregation and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based post-injury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multi-modal measure of network diffusion model propagation time captures the interplay between these two mechanisms.\n\nAbbreviationsmild traumatic brain injury (mTBI), structural connectome (SC), functional connectome (FC), network diffusion (ND), functional MRI (fMRI), diffusion MRI (dMRI), principal component analysis (PCA), partial least squares regression (PLSR), confidence interval (CI), Attention Network Test (ANT), California Verbal Learning Test II (CVLT-II), Coma Recovery Scale - Revised (CRS-R)

neuroscience↗

bioRxiv2018

Transcriptional burst initiation and polymerase pause release are key control points of transcriptional regulation

Transcriptional regulation occurs via changes to the rates of various biochemical processes. Sequencing-based approaches that average together many cells have suggested that polymerase binding and polymerase release from promoter-proximal pausing are two key regulated steps in the transcriptional process. However, single cell studies have revealed that transcription occurs in short, discontinuous bursts, suggesting that transcriptional burst initiation and termination might also be regulated steps. Here, we develop and apply a quantitative framework to connect changes in both Pol II ChIP-seq and single cell transcriptional measurements to changes in the rates of specific steps of transcription. Using a number of global and targeted transcriptional regulatory perturbations, we show that burst initiation rate is indeed a key regulated step, demonstrating that transcriptional activity can be frequency modulated. Polymerase pause release is a second key regulated step, but the rate of polymerase binding is not changed by any of the biological perturbations we examined. Our results establish an important role for transcriptional burst regulation in the control of gene expression.

systems biology↗