Biology subjects

Narayanaswamy, A.

Publications and source records attributed to Narayanaswamy, A..

3 recordsLinked to original sources

bioRxiv2020

THE DYNAMIC INTERPLAY BETWEEN HOMEODOMAIN TRANSCRIPTION FACTORS AND CHROMATIN ENVIRONMENT REGULATES PRONEURAL FACTOR OUTCOMES

Generation of neurons of vast diversity involves early spatial and temporal patterning of the neuronal precursors by morphogenic gradients and combinatorial expression of transcription factors. While the proneuronal function of the basic-helix-loop-helix (bHLH) transcription factor Ngn2 is well established, its role in neuronal subtype specification remains unclear. Here, we found that coexpressing NGN2 with the forebrain homeobox factor EMX1 converts human pluripotent stem cells into a highly homogeneous glutamatergic forebrain neurons without partial cholinergic and monoaminergic gene programs observed in cells infected with NGN2 only. Our molecular characterization revealed that transcriptional output and genomic targeting of Ngn2 is altered by co-factors such as EMX1 explaining the more focused subtype specification. Ngn2 function is less modified by the chromatin environment and does not affect regionalization of pre-patterned neural progenitors. These results enable improved strategies for generating a plethora of defined neuronal subpopulations from pluripotent stem cells for therapeutic or disease-modeling purposes. HighlightsO_LINGN2 converts human ES cells into glutamatergic neurons some of which co-express a partial cholinergic program C_LIO_LINGN2 directly binds to and activates ISL1 in ES cells which together with PHOX2A/B induce cholinergic genes C_LIO_LIAnterior-posterior regionalization affects NGN2 binding and transcriptional output but does not focus subtype specification C_LIO_LIForebrain homeobox factors including EMX1 and FOXG1 redirect NGN2 chromatin binding and repress posterior and cholinergic genes, resulting in homogeneous forebrain excitatory neurons C_LI

genomics↗

bioRxiv2020

Deep learning and automated Cell Painting reveal Parkinson's disease-specific signatures in primary patient fibroblasts

Drug discovery for diseases such as Parkinsons disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinsons disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platforms robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinsons disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

cell biology↗

bioRxiv2020

Batch Equalization with a Generative Adversarial Network

Advances in automation and imaging have made it possible to capture large image datasets for experiments that span multiple weeks with multiple experimental batches of data. However, accurate biological comparisons across the batches is challenged by the batch-to-batch variation due to uncontrollable experimental noise (e.g., different stain intensity or illumination conditions). To mediate the batch variation (i.e. the batch effect), we developed a batch equalization method that can transfer images from one batch to another while preserving the biological phenotype. The equalization method is trained as a generative adversarial network (GAN), using the StarGAN architecture that has shown considerable ability in doing style transfer for consumer images. After incorporating an additional objective that disentangles batch effect from biological features using an existing GAN framework, we show that the equalized images have less batch information as determined by a batch-prediction task and perform better in a biologically relevant task (e.g., Mechanism of Action prediction).

cell biology↗