About 📝

Nikhil Nageshwar Inturi

Data Scientist and Neuroinformatics Intern at the Center for Advanced Pain Studies | Machine Learning in Biopharma R&D and Drug Discovery

🚀 Since childhood, I have been deeply fascinated by automobiles 🚗 and computers 💻. I vividly recall my first attempts at making a Java program print "Hello World!" Now, it feels almost magical to command a computer to perform tasks with just a few lines of code.

🎓 I earned my bachelor's degree at M.S. Ramaiah Institute of Technology in Bangalore, India. During my undergraduate studies, I developed a keen interest in material science, which led me to collaborate with Dr. Vishwanath Koti to investigate the electrical ⚡ and thermal 🔥 conductivity of copper and multiwall carbon nanotube composites during my junior and senior years.

📚 Currently, I'm pursuing my master's in Business Analytics and Data Science at the University of Texas at Dallas (UTD) while interning in Data Science and Neuroinformatics at UTD's Center for Advanced Pain Studies (CAPS) 🧠. At CAPS, I apply machine learning and bioinformatics 🧬 to automate various genomic analyses, significantly reducing processing times. I'm also working on a Neuron Detection project to further streamline the 10X-Visium pipeline.

💼 I've had the privilege of working with several distinguished researchers, including Dr. Theodore J. Price, Dr. Diana F. Tavares, and Dr. Eric Christopher Meyers at UTD, as well as collaborating with labs across the globe 🌍. Before coming to the U.S., I spent 4.5 years in India's tech industry. At Infosys Ltd., I worked in consulting and development, notably for BMW and the Infosys Research Unit. There, I honed my data science skills, developing enterprise applications and algorithms, and spearheading containerization efforts that dramatically improved deployment efficiency. Later, at Aganitha, I worked as a Full-Stack Data Scientist on AAV Capsid Engineering 🧬, setting up the midstream and downstream analysis. I developed clustering models aimed at identifying optimal golden templates and predicting effective 7-mer sequences for blood-brain barrier transduction 🧠.

❤️ I carry the same passion as when I was a kid—to learn and contribute in the field of data science and bioinformatics. There's always more to learn 📖, more challenges to overcome 🏁, and more to create ✨.

Work Experience 👨🏻‍💻

Center for Advanced Pain Studies, The University of Texas at Dallas
Feb 2022 – Present

As a Data Science and Neuroinformatics Intern at Center for Advanced Pain Studies (Dr. Price, Dr. Tavares, and Dr. Meyers), I had the opportunity to work on several pipelines and interact with some amazing researchers who are exemplars in the pain field. Here is a peek into my work here as an Intern here at Center for Advance Pain Studies(CAPS):

Utilized advanced image classification and segmentation techniques to enhance neuron detection in human, mouse, and baboon neuronal images as part of the Neuron Detection project. This approach contributed to a 15% improvement in barcode detection accuracy and facilitated the generation of precise neuronal barcode files automatically for the Visium workflow.
Automated the Visium(spatial transcriptomics) analysis pipeline, which involves several steps using bash, Python, and R (Seurat analysis) scripts, as well as NextFlow/Cromwell scripts. My focus was on optimizing spatial data analysis for Visium by integrating neuron detection models through extensive cross-validation, hyper-parameter tuning, and albumentations.
Developed and optimized scripts for the comprehensive analysis of diverse biological datasets, including Single-cell, Visium (spatial), ATAC-sequencing, and Bulk RNA-sequencing using Nextflow and Docker. This enhancement led to a 60% increase in analytical efficiency, significantly accelerating the identification of differentially expressed genes and biomarkers.
Collaborated with cross-functional teams from multiple research labs, such as Arkady's Lab at McGill, to streamline computational pipelines using workflows and containers for improved efficiency.
Explored the analysis of non-coding RNA datasets, particularly microRNAs, to understand their role in regulating gene expression and their implications in neuropathy and chronic pain pathways.

Spring 2024 Spring 2024 + Fall 2024

Aganitha Cognitive Solutions, Hyderabad, India
Jun 2022 – Nov 2023

Infosys Ltd, Hyderabad, India
Sep 2018 – Jun 2022

Publications 📚

Abstract: Human dermal sleeping nociceptors display ongoing activity in neuropathic pain, affecting 10% of the population. Despite advances in rodents, a molecular marker for these mechano-insensitive C-fibers (CMis) in human skin remains elusive, preventing targeted therapy. In this translational Patch-seq study, we combine single-cell transcriptomics following electrophysiological characterization with single-nucleus and spatial transcriptomics from pigs and humans. We functionally identified CMis in pig sensory neurons with patch-clamp using adapted protocols from human microneurography. We identified oncostatin-M-receptor (OSMR) and somatostatin (SST) as marker genes for CMis. Following dermal injection in healthy human volunteers, oncostatin-M, the ligand of OSMR, exclusively modulates CMis. We identified the entire molecular architecture of human dermal sleeping nociceptors, providing new therapeutic targets and the basis for a mechanistic understanding of neuropathic pain.
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Abstract: Sensitization of spinal nociceptive circuits plays a crucial role in neuropathic pain. This sensitization depends on new gene expression that is primarily regulated via transcriptional and translational control mechanisms. The relative roles of these mechanisms in regulating gene expression in the clinically relevant chronic phase of neuropathic pain are not well understood. Here, we show that changes in gene expression in the spinal cord during the chronic phase of neuropathic pain are substantially regulated at the translational level. Downregulating spinal translation at the chronic phase alleviated pain hypersensitivity. Cell-type-specific profiling revealed that spinal inhibitory neurons exhibited greater changes in translation after peripheral nerve injury compared to excitatory neurons. Notably, increasing translation selectively in all inhibitory neurons or parvalbumin-positive (PV+) interneurons, but not excitatory neurons, promoted mechanical pain hypersensitivity. Furthermore, increasing translation in PV+ neurons decreased their intrinsic excitability and spiking activity, whereas reducing translation in spinal PV+ neurons prevented the nerve injury-induced decrease in excitability. Thus, translational control mechanisms in the spinal cord, particularly in inhibitory neurons, play a role in mediating neuropathic pain hypersensitivity.
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Abstract: Diabetic peripheral neuropathy (DPN) is a prevalent complication of diabetes mellitus that is caused by metabolic toxicity to peripheral axons. We aimed to gain deep mechanistic insight into the disease process using bulk and spatial RNA sequencing on tibial and sural nerves recovered from lower leg amputations in a mostly diabetic population. First, our approach comparing mixed sensory and motor tibial and purely sensory sural nerves shows key pathway differences in affected nerves, with distinct immunological features observed in sural nerves. Second, spatial transcriptomics analysis of sural nerves reveals substantial shifts in endothelial and immune cell types associated with severe axonal loss. We also find clear evidence of neuronal gene transcript changes, like PRPH, in nerves with axonal loss suggesting perturbed RNA transport into distal sensory axons. This motivated further investigation into neuronal mRNA localization in peripheral nerve axons generating clear evidence of robust localization of mRNAs such as SCN9A and TRPV1 in human sensory axons. Our work gives new insight into the altered cellular and transcriptomic profiles in human nerves in DPN and highlights the importance of sensory axon mRNA transport as an unappreciated potential contributor to peripheral nerve degeneration.
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Abstract: Gene expression is influenced by chromatin architecture via controlled access of regulatory factors to DNA. To better understand gene regulation in the human dorsal root ganglion (hDRG) we used bulk and spatial transposase-accessible chromatin technology followed by sequencing (ATAC-seq). Using bulk ATAC-seq, we detected that in females diverse differentially accessible chromatin regions (DARs) mapped to the X chromosome and in males to autosomal genes. EGR1/3 and SP1/4 transcription factor binding motifs were abundant within DARs in females, and JUN, FOS and other AP-1 factors in males. To dissect the open chromatin profile in hDRG neurons, we used spatial ATAC-seq. The neuron cluster showed higher chromatin accessibility in GABAergic, glutamatergic, and interferon-related genes in females, and in Ca2+- signaling-related genes in males. Sex differences in transcription factor binding sites in neuron-proximal barcodes were consistent with the trends observed in bulk ATAC-seq data. We validated that EGR1 expression is biased to female hDRG compared to male. Strikingly, XIST, the long-noncoding RNA responsible for X inactivation, hybridization signal was found to be highly dispersed in the female neuronal but not non-neuronal nuclei suggesting weak X inactivation in female hDRG neurons. Our findings point to baseline epigenomic sex differences in the hDRG that likely underlie divergent transcriptional responses that determine mechanistic sex differences in pain.
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Abstract: Diabetic Peripheral Neuropathy (DPN) stands out as a very common and clinically consequential complication of diabetes. DPN is characterized by nerve conduction impairment, dieback of nerve endings from the skin, spontaneous pain, and numbness in extremities. In this study, we conducted single-nucleus RNA sequencing on human dorsal root ganglion (DRG) tissues recovered from organ donors with a documented history of diabetes and neuropathic pain. Employing 10x Genomics technology, we performed single-nucleus RNA sequencing on eight human DRG samples each from individuals with DPN or healthy donors. Our primary objectives encompassed elucidating distinct populations of neurons and comprehending their transcriptomic variations between healthy and DPN DRGs. Additionally, we sought to characterize the diversity of transcriptomic changes in non-neuronal cells and explore their neuro-immune interactions in DPN. Our analysis revealed significant alterations in non-neuronal cell populations, including T cells, macrophages, and adipocytes within human DRG of DPN donors. Our work presents a comprehensive single-cell transcriptomic landscape of human DRGs in DPN donors, providing insights into molecular profile changes associated with DPN. The interactions between neuronal and non-neuronal cells could offer valuable insights into the underlying mechanisms of this condition, assisting in the development of novel therapeutics to alleviate DPN. Funded by National Institute of Health (U19 NS130608).
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Abstract: Hyperalgesic priming is a model system that has been widely used to understand plasticity in painful stimulus-detecting sensory neurons, called nociceptors. A key feature of this model system is that following priming, stimuli that do not normally cause hyperalgesia now readily provoke this state. We hypothesized that hyperalgesic priming occurs due to reorganization of translation of mRNA in nociceptors. To test this hypothesis, we used paclitaxel treatment as the priming stimulus and translating ribosome affinity purification (TRAP) to measure persistent changes in mRNA translation in Nav1.8+ nociceptors. TRAP sequencing revealed 161 genes with persistently altered mRNA translation in the primed state. We identified Gpr88 as upregulated and Metrn as downregulated. We confirmed a functional role for these genes, wherein a GPR88 agonist causes pain only in primed mice and established hyperalgesic priming is reversed by Meteorin. Our work demonstrates that altered nociceptor translatomes are causative in producing hyperalgesic priming.
Show Publication The Journal of the International Association for the Study of Pain Download Paper

Education 🎓

The University of Texas at Dallas

Master’s in Business Analytics and Artificial Intelligence

Jan 2023 - Present

Purdue University

Post Graduate Program in AI and Machine Learning

Oct 2020 - Nov 2021

Ramaiah Institute of Technology

Bachelor of Engineering in Mechanical Engineering

Aug 2014 - June 2018

Skills Journey ⚒️

Data Analytics

Data Science, Machine Learning, Computer Vision and Deep Learning, Natural Language Processing, Bokeh Visuvalization

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Programming

Python, R-programming, Shell Scripting, Java, SQL, NoSQL, DQL

Tools & Technologies

Cloud Computing: Azure and AWS, Docker, Kubernetes, Cromwell, Nextflow, Nginx, CI/CD Pipelines, Documentum (ECM Software), Agile Certified Practitioner

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Web Servers

Tornado, Django, Flask

Other Skills

Bioinformatics, Omics, Genomics, Transcriptomics, Bulk RNA-sequemcing Analysis (DE Genes), Visium Analysis(Spatial Transcriptomics Data), ATAC-sequencing, Single-cell Analysis, AAV Capsid Engineering

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