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Aarthi Talla headshot

Aarthi Talla

Bioinformatics Principal


Aarthi Talla is currently a Bioinformatics Principal at the Allen Institute for Immunology. She graduated with a master’s degree in Bioinformatics from Georgia Institute of Technology in 2010. Since 2011, she has more than 8 years of professional experience as a computational biologist, more specifically as a Systems Immunologist. She was involved in understanding immune mechanisms associated with chronic HIV infection and the latent HIV reservoir, understanding the mechanisms that drive heterogenous responses to clinical immuno-therapies in patients with HIV and refractory malignancies. She was also involved in identifying immune correlates of response to CAR T-cell therapy. In order to do so, she applies systems biology, statistical and machine learning approaches to integrate several multi-omics data measured on the same patients, such as bulk and single-cell transcriptomics, Flow cytometry/Mass cytometry, proteomics, metabolomics and the microbiome to identify biomarkers of disease or understanding responses to immunotherapies at a ‘systemic level’. Currently her role at the institute is to identify immune mechanisms associated with progression and development of Rheumatoid Arthritis (RA) in patients who are clinically pre-disposed to developing RA. In addition, with the unfortunate COVID-19 pandemic and the need to understand how it has impacted different people in different ways, she is involved in studying how different immune responses are changing in due course of infection and dissecting the age and gender related differences in immune signaling pathways over time. She believes that understanding the immune system at a systemic level in context of disease will aid in effective interventions to eradicate disease and benefit the scientific and patient population.

Research Focus:

Aarthi is co-leading the efforts on establishing strategies for analyzing single-cell RNA sequencing data. She is involved in establishing an analytical framework that could be leveraged by a wide variety of scientists, both non-coders and coders. She is involved in establishing an unsupervised learning framework for analyzing high dimensional flow cytometry data in an unbiased and an objective manner. One of the major projects she is currently involved in, is understanding the transient changes in immune signaling responses in patients infected with SARS-CoV2 over a longitudinal time course starting from very early days since symptom onset. The major focus has been to understand and dissect the heterogeneity in immune responses over time that could be attributed to age, gender, comorbidities etc.

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