About

Santosh Paidi is a Technical Development Principal Scientist at Genentech. His postdoctoral research in the labs of Professors Na Ji and Xiaohua Gong at the University of California, Berkeley were directed towards the development of optical imaging systems for in vivo ocular imaging of mouse genetic models of eye diseases. His Ph.D. research in Professor Ishan Barman’s lab at Johns Hopkins University focused on the development of chemical imaging methods based on Raman spectroscopy and machine learning for quantitative study of cancer progression and therapy. His work in these areas has resulted in over 25 publications and recognized by several awards.

Education and CV

Click here for detailed Curriculum Vitae

Click here for Google Scholar profile

  • Ph.D. in Mechanical Engineering — Advisor: Dr. Ishan Barman, Johns Hopkins University, USA (2014 - 2020)
  • M.S.E. in Mechanical Engineering — Johns Hopkins University, USA (2017)
  • B.Tech. in Mechanical Engineering with a minor in Aerospace Engineering — Indian Institute of Technology Bombay, India (2014)

Selected Research Projects

In vivo imaging of ocular lenses with adaptive optical two photon fluorescence microscopy

My postdoctoral training at the University of California, Berkeley was focused on the development of two photon fluorescence microscopy (TPFM) and adaptive optics for in vivo imaging of ocular lenses in genetic mouse models. Traditionally, microscopic studies of the cellular organization of mammalian lenses have been carried out ex vivo on dissected lenses, which eliminates both in vivo environment for supporting lens internal circulation and attached zonules for regulating lens accommodation. Our work has demonstrated that two photon fluorescence microscopy and adaptive optics can enable in vivo visualization of lens cells in transgenic mice that express fluorescent proteins in lens cell membranes. We employed direct wavefront sensing to measure and correct aberrations to image cellular organization deep inside the anterior part of the lens and discovered novel features in different regions up to near the lens core that have not been observed in prior ex vivo studies. These studies demonstrated the importance of in vivo imaging in preserving the native biological context in live animals and opened a new route for observing changes in lens cell morphology during normal development and pathological processes.
Paidi SK et al., Investigative Ophthalmology and Visual Science (iOVS), 2023

Raman spectroscopy reveals phenotype switches in breast cancer metastasis

By studying isogenic murine breast cancer cell lines of varying metastatic abilities and high- metastatic 4T1 cells lines silenced for genes crucial to metastasis using CRISPR/Cas9 and shRNA, we found new biological insights into compositional differences in the stroma of tumors of varying metastatic abilities. We showed that Raman spectral signatures not only captured the differences in the lipid and collagen content of tumors of progressively increasing metastatic abilities but also allowed us to observe the changes in these markers associated with the loss of metastatic ability due to the silencing of the expression of TWIST1, FOXC2, and CXCR3 in highly metastatic 4T1 cells. Our work in these areas helped to expand the scope of optical spectroscopy to early detection of metastatic disease in secondary organs and prediction of response to therapy in advance of morphological manifestation.
Paidi SK et al., Theranostics, 2022

Optical cellular phenotyping using label-free optical spectroscopy and quantitative phase imaging

Rapid identification and staging of single cancer cells is very critical for early detection of disease. Further prognostic phenotyping of single cells, including their future metastatic propensities, can inform therapeutic choices. Our work in this area at cellular level sought to develop methods based on the fusion of Raman spectroscopy and quantitative phase imaging (QPI), that respectively provide chemical and morphological attributes of biological specimen, for distinguishing closely related phenotypes associated with disease progression and metastasis at single-cell analytical resolution. By developing a coarse Raman mapping strategy, we have circumvented the bottleneck of long acquisition times required for high-resolution Raman microscopy and acquired sufficient data for single-cell metastatic phenotyping of isogenic breast cancer cells. We showed that random forest models built on the coarse Raman maps of single cells were able to distinguish the circulating tumor cell (CTC) and lung metastatic cell (LMC) variants from the parental MDA-MB-231 breast cancer cells with single-cell analytical resolution. We also employed a set of B-cell acute lymphoblastic leukemia (B-ALL) cell lines and built models based on Raman spectroscopy and QPI data to predict the stage of each cell line during the disease progression and distinguish them from healthy control B-cells.
Paidi SK et al., Biosensors and Bioelectronics, 2020 Paidi SK et al., Biosensors and Bioelectronics, 2021

Raman Spectroscopy for Prediction of Response to Radiation and Immunotherapy

Our label-free spontaneous Raman spectroscopy study using lung tumor and head and neck tumor xenografts of known radiation sensitivity in mice has revealed microenvironmental changes induced by exposure to clinically relevant doses of radiation to sensitive and resistant variants. Changes in collagen, glycogen and lipid content, as probed by their unique Raman spectral signatures, facilitate the development of a decision algorithm, which accurately differentiates radiosensitive tumors prior to and following radiation therapy. We have also studied murine models of colon cancer to show that Raman spectroscopy can identify tumor response to immunotherapy using different immune checkpoint inhibitors.
Paidi SK et al., Cancer Research, 2021 Paidi SK et al., Cancer Research, 2019

Molecular analysis of cancer progression using label-free Raman spectroscopy

Optical spectroscopy allows us to define cancer in biomolecular rather than morphological terms and facilitates unbiased interrogation of the specimen without the need for exogeneous labeling and the knowledge of specific targets. We have demonstrated the ability to detect premetastatic changes in the lungs of mice bearing breast tumors, in advance of tumor cell seeding, using Raman spectroscopy and multivariate data analysis. The measurements showed reliable differences in the collagen and proteoglycan features of the premetastatic lungs which uniquely identify the metastatic potential of the primary tumor and hints at a continuous premetastatic niche formation model.
Paidi SK et al., Cancer Research, 2017

Awards

  1. Frederick Bettelheim Award 2022
  2. AACR Scholar-in-Training Award 2022
  3. Barbara Stull Graduate Student Award 2019
  4. Tony B. Academic Travel Award 2019 and 2018
  5. Coblentz Student Award 2018
  6. SLAS Graduate Education Fellowship Grant 2018
  7. SPIE Optics and Photonics Education Scholarship 2018
  8. Tomas A. Hirschfeld Scholar Award 2017
  9. Molecular Medicine Tri-Conference Student Fellowship 2015
  10. Whiting School Doctoral Fellowship at JHU 2014
  11. Mechanical Engineering Departmental Fellowship at JHU 2014
  12. Undergraduate Research Award at IIT Bombay 2013
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Selected Publications

Google Scholar link for most updated list with links

  1. Paidi SK, Zhang Q, Yang Y, Xia CH, Ji N, Gong X. "Adaptive optical two-photon fluorescence microscopy probes cellular organization of ocular lenses in vivo", Investigative Opthalmology and Visual Science, 64(20), 2023.
  2. Paidi SK*, Troncoso JR*, Harper MG, Liu Z, Nguyen KG, Ravindranathan S, Rebello L, Lee DE, Ivers JD, Zaharoff DA, Rajaram N, Barman I. "Raman spectroscopy reveals phenotype switches in breast cancer metastasis", Theranostics, 12(12), 5351-63, 2022.
  3. Paidi SK*, Troncoso JR*, Raj P, Diaz PM, Ivers JD, Lee DE, Avaritt NL, Gies AJ, Quick CM, Byrum SD, Tackett AJ Rajaram N, Barman I. "Raman spectroscopy and machine learning reveals early tumor microenvironmental changes induced By immunotherapy", Cancer Research, 81(22), 5745–55, 2021.
  4. Paidi SK*, Shah V*, Raj P, Glunde K, Pandey R, Barman I. "Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential", Biosensors and Bioelectronics, 2021 175, 112863, 2021.
  5. Paidi SK*, Raj P*, Bordett R, Zhang C, Karandikar SH, Pandey R, Barman I. "Raman and quantitative phase imaging allow morphomolecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia", Biosensors and Bioelectronics, 190, 113403, 2021.
  6. Tanwar S, Paidi SK, Prasad R, Pandey R, Barman I. “Advancing Raman Spectroscopy from Research to Clinic: Translational Potential and Challenges”, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 260, 1199572, 2021.
  7. Ahmed I, Khan MS, Paidi SK, Liu Z, Zhang C, Liu Y, Baloch GA, Law AWL, Zhang Y, Barman I, Lau C, "Laser induced breakdown spectroscopy with machine learning reveals lithium-induced electrolyte imbalance in the kidneys”, Journal of Pharmaceutical and Biomedical Analysis 194, 113805, 2021.
  8. Ahmed I, Ma V, Liu Y, Khan MS, Liu Z, Zhang C, Paidi SK, Manno FAM, Amjad N, Manno SHC, Ahmed R, Law, AWL, Ali A, Raza F, Zhang Y, Cho WCS, Barman I, Alda M, Bergink V, Lau C."Lithium from breast-milk inhibits thyroid iodine uptake and hormone production, which are remedied by maternal iodine supplementation", Bipolar Disorders, 23(6), 615-25, 2021.
  9. Paidi SK, Pandey R, Barman I. "Emerging trends in biomedical imaging and disease diagnosis using Raman spectroscopy", Molecular and Laser Spectroscopy Volume 2, 623-52, 2020.
  10. Paidi SK, Pandey R, Barman I. "Medical applications of Raman spectroscopy", Encyclopedia of Analytical Chemistry, 1-21, 2020.
  11. Ayyappan V*, Chang V*, Zhang C*, Paidi SK*, Bordett R, Liang T, Barman B, Pandey R. "Identification and staging of B-cell acute lymphoblastic leukemia using quantitative phase imaging and machine learning", ACS Sensors, 5(10), 3281–89, 2020.
  12. Ming Li, Lin H*,Paidi SK*, Mesyngier N*, Preheim S, Barman I. "A fluorescent and surface-enhanced Raman spectroscopic dual-modal aptasensor for sensitive detection of cyanotoxins", ACS Sensors, 5(5), 1419-26, 2020.
  13. Paidi SK*(*equal contributions), Diaz PM*, Dadgar S*, Jenkins SV, Quick CM, Griffin RJ, Dings RPM, Rajaram N, Barman I. "Label-free Raman spectroscopy reveals tumor microenvironmental signatures of radiation resistance", Cancer Research, 79(8), 2054-64, 2019. (See media section for related press coverage)
  14. Xu W, Paidi SK, Qin Z, Huang Q, Yu CH, Pagaduan J, Buehler MJ, Barman I, Gracias DH. "Self-folding hybrid graphene skin for 3D biosensing", Nano Letters, 19(3), 1409-17, 2019.
  15. Li M, Paidi SK, Sakowski E, Preheim S, Barman I. "Ultrasensitive Detection of Hepatotoxic Microcystin Production from Cyanobacteria Using Surface-Enhanced Raman Scattering (SERS) Immunosensor", ACS Sensors, 4(5), 1203-10, 2019.
  16. Rizwan A*, Paidi SK*, Zheng C*, Cheng M, Fan Z, Barman I, Glunde K. "Mapping the genetic basis of breast microcalcifications and their role in metastasis", Scientific Reports, 8:11067, 2018.
  17. Paidi SK*, Rizwan A*, Zheng C*, Cheng M, Glunde K, Barman I. "Label-free Raman spectroscopy detects stromal adaptations in pre-metastatic lungs primed by breast cancer", Cancer Research, 77(2), 247-56, 2017.
  18. Pandey R, Paidi SK, Valdez TA, Zhang C, Spegazzini N, Dasari RR, Barman I. "Noninvasive monitoring of blood glucose with Raman spectroscopy", Accounts of Chemical Research, 50(2), 264-72, 2017.
  19. Siddhanta S, Paidi SK, Bushley K, Prasad R, Barman I. "Exploring morphological and biochemical linkages in fungal growth with label-free light sheet microscopy and Raman spectroscopy", ChemPhysChem, 18(1), 72-8, 2017. (Journal back cover)
  20. Jin Q*, Li M*, Polat B, Paidi SK, Dai A, Zhang A, Padaguan J, Barman I, Gracias DH. "Mechanical trap surface enhanced Raman spectroscopy (MTSERS) for 3D surface molecular imaging of single live cells," Angewandte Chemie International Edition, 56(14), 3822-26, 2017.
  21. Paidi SK, Siddhanta S, Strouse R, McGivney JB, Larkin C, Barman I. "Rapid identification of biotherapeutics with label-free Raman spectroscopy", Analytical Chemistry, 88(8), 4361-8, 2016.
  22. Myakalwar AK, Anubham SK, Paidi SK, Barman I, Gundawar MK. "Real-time fingerprinting of structural isomers using laser induced breakdown spectroscopy", Analyst, 141(10), 3077-83, 2016.
  23. Zheng C*, Shao W*, Paidi SK, Han B, Fu T, Wu D, Bi L, Xu W, Fan Z, Barman I. "Pursuing shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) for concomitant detection of breast lesions and microcalcifications", Nanoscale, 7, 16960-8, 2015.
  24. Pandey R*, Paidi SK*, Kang JW, Spegazzini N, Dasari RR, Valdez TA, Barman I. "Discerning the differential molecular pathology of proliferative middle ear lesions using Raman spectroscopy", Scientific Reports, 5:13305, 2015.
  25. Paidi SK, Bhavaraju A, Akram M, Kumar S. "Effect of N2/CO2 dilution on Laminar Burning Velocity of H2-Air Mixtures at High Temperatures", International Journal of Hydrogen Energy, 38(31), 13812-21, 2013.
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Media Coverage

  1. Raman spectroscopy shows how immunotherapy changes tumours, Spectroscopy Europe, 2021.
  2. Raman spectroscopy: new strategy of evaluating metastatic risk in breast cancer, AzoOptics, 2021.
  3. Researchers pioneer method to examine how immunotherapy changes tumors, The Hub at Johns Hopkins, 2021.
  4. Imaging tool helps doctors predict how cancer will respond to radiation, Inside Science News Service, 2019.
  5. Raman spectroscopy predicts radiation resistance, Physics World, 2019.
  6. Detecting differences between radiation-sensitive and resistant tumors, Technology Networks, 2019.
  7. Imaging technique finds differences between radiation-sensitive and resistant tumors, Science Daily, 2019.
  8. Shedding light on resistance to radiation therapy, The Hub at Johns Hopkins, 2019.
  9. Method may predict if radiation will work on tumors, Futurity: Research News, 2019.
  10. Shedding light literally on resistance to radiation therapy, Medical Xpress, 2019.
  11. 2018 SLAS Graduate Education Fellowship Grant recipient fosters SERS diagnostic assay concept, SLAS Electronic Laboratory Neighborhood, 2018.
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Contact

For more information about research topics, reasonable requests for author-copies of specific publications and talk invitations, please contact me directly at the email below.

Email: santosh175 [at] gmail.com