Biology subjects

Barman, I.

Publications and source records attributed to Barman, I..

4 recordsLinked to original sources

bioRxiv2021

DNA Self-Assembled Plasmonic Nanodiamonds for Biological Sensing

Nitrogen-vacancy (NV) centers in diamonds are promising solid-state quantum emitters for developing superior biological imaging modalities. They possess desired bio-compatibility, photostability and electronic spin-related photophysical properties that are optically accessible at room temperature. Yet, bare nanodiamond-based imaging modalities are limited by the brightness and temporal resolution due to the intrinsically long lifetime of NV centers. Moreover, it remains a technological challenge using top-down fabrication to create freestanding hybrid nanodiamond imaging probes with enhanced performance. In this study, we leverage the bottom-up DNA self-assembly to develop a hybrid plasmonic nanodiamond construct, which we coin as the plasmon-enhanced nanodiamond (PEN), for biological imaging. The PEN nano-assembly features a closed plasmonic nanocavity that completely encapsulates a single nanodiamond, thus enabling the largest possible plasmonic enhancement to accelerate the emission dynamics of NV centers. Creation of the PEN nano-assembly is size-independent, so is its broadband scattering spectrum that is optimally overlapped with the emission spectrum of NV centers. Study of the structure-property correlation reveals that the optimal condition for emission dynamics modification is causally linked to that for a plasmonic nanocavity. The cellular internalization and cytotoxicity studies further confirm the delivery efficiency and biological safety of PEN nano-assemblies. Collectively, the PEN nano-assembly provides a promising approach for manipulating photophysical properties of solid-state quantum emitters and could serve as a versatile platform to uncover non-trivial quantum effects in biological systems.

biophysics↗

bioRxiv2021

Raman spectroscopy reveals phenotype switches in breast cancer metastasis

The accurate analytical characterization of metastatic phenotype at primary tumor diagnosis and its evolution with time are critical for controlling metastatic progression of cancer. Here, we report a label-free optical strategy using Raman spectroscopy and machine learning to identify distinct metastatic phenotypes observed in tumors formed by isogenic murine breast cancer cell lines of progressively increasing metastatic propensities. Our Raman spectra-based random forest analysis provided evidence that machine learning models built on spectral data can allow the accurate identification of metastatic phenotype of independent test tumors. By silencing genes critical for metastasis in highly metastatic cell lines, we showed that the random forest classifiers provided predictions consistent with the observed phenotypic switch of the resultant tumors towards lower metastatic potential. Furthermore, the spectral assessment of lipid and collagen content of these tumors was consistent with the observed phenotypic switch. Overall, our findings indicate that Raman spectroscopy may offer a novel strategy to evaluate metastatic risk during primary tumor biopsies in clinical patients.

bioengineering↗

bioRxiv2021

Reagent-free Raman and quantitative phase imaging offer a unique morpho-molecular platform for recognition of malignancy and stages of B-cell acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common malignancies which account for nearly one-third of all pediatric cancers. The current diagnostic assays are time-consuming, labor-intensive, and require expensive reagents. Here, we report a label-free approach featuring diffraction phase imaging and Raman microscopy that can retrieve both morphological and molecular attributes for label-free optical phenotyping of individual B cells. By investigating leukemia cell lines of early and late stages along with the healthy B cells, we show that phase image can capture subtle morphological differences among the healthy, early, and late stages of leukemic cells. By exploiting its biomolecular specificity, we demonstrate that Raman microscopy is capable of accurately identifying not only different stages of leukemia cells, but also individual cell lines at each stage. Overall, our study provides a rationale for employing this hybrid modality to screen leukemia cells using the widefield QPI and using Raman microscopy for accurate differentiation of early and late-stage phenotypes. This contrast-free and rapid diagnostic tool exhibits great promise for clinical diagnosis and staging of leukemia in the near future.

bioengineering↗

bioRxiv2020

Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive underscoring the need to marry emerging imaging techniques with tumor biology. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging the molecular specificity of Raman spectroscopy, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also leverage multivariate curve resolution - alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

bioengineering↗