Biology subjects

Barman, I.

Publications and source records attributed to Barman, I..

16 recordsLinked to original sources

bioRxiv2026

Cellular Density Homeostasis Is Governed by an Anisotropic Attractor and Predictive Feedback Control

Cell density is a fundamental biophysical property of living cells, yet the dynamical architecture underlying density homeostasis remains unknown. Here, we reconstruct the geometry, dynamics, and control structure governing cellular density regulation from single-cell mass-volume measurements acquired during recovery from osmotic perturbation. We identify a persistent anisotropic attractor in mass-volume state space that permits coordinated density-preserving fluctuations while strongly suppressing density-changing deviations. Optimal transport analysis reveals directed recovery trajectories confined to this attractor, enabling inference of an effective density-restoring control law. Density deviations are associated with coordinated changes in biomass accumulation and volume expansion that together generate an effective density-restoring control law. Moreover, density error contains significant predictive information regarding future corrective behavior, revealing an information-rich component of cellular regulation. Together, these findings establish cellular density homeostasis as an emergent dynamical control process and demonstrate how attractor reconstruction, optimal transport, and information theory can be combined to uncover the organizing principles of biological homeostasis.

biophysics↗

bioRxiv2025

Affordable plasmonic biosensing: democratizing SERS with scalable, field-compatible substrate fabrication

Efficient and accurate plasmonic biosensing in the field remains a significant challenge. Despite its potential to revolutionize point-of-care (PoC) diagnostics through its unparalleled sensitivity and precise molecular fingerprinting capabilities, adoption of traditional surface-enhanced Raman spectroscopy (SERS) in the field has not proven feasible yet. High production and material costs, complex fabrication methods, reliance on specialized equipment, and persistent issues with sensor stability and reproducibility continue to impede development for PoC use. Addressing these challenges, this study innovates a democratized and cost-effective fabrication kit that enables the production of SERS substrates using commonly available materials and straightforward electrochemistry techniques without compromising on sensitivity and reproducibility. Importantly, this method leverages commercially available bottled water and simple battery-powered fabrication, thereby eliminating reliance on a power grid and enabling the local production of biosensors in resource-restricted and conflict-affected areas. The cost of producing the fabrication kit is $39.54 with raw materials purchased at bulk retail prices, while the cost of consumables for fabricating each test is just 1.33{cents}. To ensure real-world feasibility, we conducted a comprehensive reproducibility analysis, where consistent plasmonic enhancement was observed across multiple production batches. Furthermore, we demonstrated their efficacy in two critical applications: the rapid detection of bacteria and pesticides. We detect trace levels of pesticides such as Thiram and Thiabendazole down to 0.1 ppm using a digital SERS approach. We also demonstrated the identification of bacteria isolated from culture, namely Escherichia coli and Bacillus subtilis. We envision that this label-free, high-sensitivity substrate, when paired with portable Raman spectrometers, could open doors for a new era of field-deployable biosensing, paving the way for its adoption for a plethora of applications, from public health to food testing.

bioengineering↗

bioRxiv2025

Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media

Maintaining consistent quality in biopharmaceutical manufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) struggle to achieve rapid and highly accurate monitoring of small molecule critical process parameters and critical quality attributes. While Raman spectroscopy holds great promise as a highly sensitive and specific bioanalytical tool for PAT applications, its conventional implementation, surface-enhanced Raman spectroscopy (SERS), is constrained by considerable temporal and spatial intensity fluctuations, limiting the achievable reproducibility and reliability. Herein, we introduce a deep learning-powered colloidal digital SERS platform to address these limitations. Rather than addressing the intensity fluctuations, the approach leverages their very stochastic nature, arising from highly dynamic analyte-nanoparticle interactions. By converting the temporally fluctuating SERS intensities into digital binary "ON/OFF" signals using a predefined intensity threshold by analyzing the characteristic SERS peak, this approach enables digital visualization of single-molecule events and significantly reduces false positives and background interferences. By further integrating colloidal digital SERS with deep learning, the applicability of this platform is significantly expanded and enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks for certain analytes. We further implement this approach for studying AMBIC 1.1, a chemically-defined, serum-free complete media for mammalian cell culture. The obtained highly accurate and reproducible results demonstrate the unique capabilities of this platform for rapid and precise cell culture media monitoring, paving the way for its widespread adoption and scaling up as a new PAT tool in biopharmaceutical manufacturing and biomedical diagnostics.

biophysics↗

bioRxiv2024

Cells Prioritize the Regulation of Cell Mass Density

A cells global physical state is characterized by its volume and dry mass. The ratio of cell mass to volume is the cell mass density (CMD), which is also a measure of macromolecular crowding and concentrations of all proteins. Using the Fluorescence eXclusion method (FXm) and Quantitative Phase Microscopy (QPM), we investigate CMD dynamics after exposure to sudden media osmolarity change. We find that while the cell volume and mass exhibit complex behavior after osmotic shock, CMD follows a straightforward monotonic recovery in 48 hours. The recovery is cell-cycle independent and relies on a coordinated adjustment of protein synthesis and volume growth rates. Surprisingly, we find that the protein synthesis rate decreases when CMD increases. This result is explained by CMD-dependent nucleoplasm-cytoplasm transport, which serves as negative regulatory feedback on CMD. The Na+/H+ exchanger NHE plays a role in regulating CMD by affecting both protein synthesis and volume change. Taken together, we reveal that cells possess a robust control system that actively regulates CMD during environmental change.

biophysics↗

bioRxiv2024

Disordered glass nanowire substrates produce in vivo-like astrocyte morphology revealed by optical diffraction tomography

Astrocytes, integral components of the central nervous system (CNS), fulfill crucial roles such as maintaining ion homeostasis, providing neuroprotection, and contributing to the blood-brain barrier. Their distinctive, star-like morphology is essential to these functions, and abnormalities in astrocyte structure are linked to numerous neurological disorders. However, our understanding of astrocyte morphology, particularly in vivo, remains limited. Traditional imaging methods, such as fluorescence microscopy, introduce challenges like restricting continuous observation and comprehensive morphological analysis. In this study, we present a novel approach utilizing optical diffraction tomography (ODT), an advanced imaging technique that generates 3D refractive index profiles, to image and quantify detailed astrocyte morphology. We demonstrate, for the first time, the application of ODT to image samples through and on disordered glass nanowire (NW) substrates, overcoming the typical challenges posed by nanostructures, which can disrupt phase reconstruction. Crucially, we show that disordered glass nanowire (NW) substrates can induce in vivo-like astrocyte morphology in cultured rat cortical astrocytes. Compared to traditional glass substrates, astrocytes grown on disordered glass NWs substrates exhibited enhanced process branching and greater total arbor length--features typically observed in their natural, in vivo state, a state of advanced maturation. This finding underscores the significant influence of substrate topography on astrocyte structure and highlights the unique potential of nanostructured environments to mimic physiological conditions. By leveraging ODT, we were able to monitor astrocyte behavior on these substrates, providing unprecedented insights into their morphological dynamics. Our study pioneers the use of nanostructured substrates for reconstructing astrocyte morphology and sets the stage for further exploration of how microenvironmental cues shape astrocyte morphology and behavior.

cell biology↗

bioRxiv2024

Sweet science: Exploring the impact of fructose and glucose on brown adipocyte differentiation using optical diffraction tomography

The thermogenic capacity of brown adipose tissue (BAT) has garnered much attention for its potential to regulate systemic energy balance. BAT depot size and function need to be tightly to prevent loss of metabolic homeostasis due to energy dissipation via non-shivering thermogenesis. While adipocyte-intrinsic mechanisms controlling thermogenesis are critical, an increasing appreciation for the role of the BAT microenvironment is emerging. For example, changes in circulating hexoses due to dietary intake have shown to impact BAT function. Here, we show that murine BAT preadipocytes metabolism is impacted when fructose is used as the sole carbon source. Similarly differentiation medium containing only fructose yield mature adipocytes with fewer lipid droplets, with a concomitant decrease in adipogenic genes. These deficiencies are also observed in human BAT preadipocytes, where cutting-edge optical imaging modalities show a decrease in total cell mass and lipid mass in fructose-only medium. Taken together, the metabolic microenvironment significantly impacts BAT growth and function, with implications for the role of diets potentially mitigating the efficacy of BAT-targeted therapies.

biochemistry↗

bioRxiv2024

Uncovering Astrocyte Morphological Dynamics Using Optical Diffraction Tomography and Shape-based Trajectory Inference

Astrocytes, integral components of the central nervous system, are increasingly recognized for their multifaceted roles beyond mere support cells. Despite their acknowledged importance, understanding the intricacies of astrocyte morphology and dynamics remains limited. Our study marks the first exploration of astrocytes using optical diffraction tomography (ODT), aiming to establish a solid foundation for their detailed characterization. It offers valuable insights into the morphological changes in postnatal rat cortical astrocytes over a 7-day in vitro period in a label-free manner. Through comprehensive analysis of 3D refractive index maps and shape characterization techniques, we elucidate the developmental trajectory and dynamic morphological transformations of astrocytes in culture. Specifically, our observations revealed increased area and transition to larger, flattened shapes, and alterations in cell volume and density, indicating shifts in cellular composition. Furthermore, by employing unsupervised clustering and pseudotime trajectory analysis, we tracked the morphological evolution of astrocytes from highly directional to evenly spread shapes. These results emphasize the dynamic nature of astrocytes. In addition, this analysis marks the first use of trajectory inference based solely on morphology for neural cell types. Future studies could employ ODT to examine the morphological dynamics and interactions of various neural cell types on other types of substrates.

cell biology↗

bioRxiv2024

Cell-TIMP: Cellular Trajectory Inference based on Morphological Parameter

Cellular morphology, shaped by various genetic and environmental influences, is pivotal to studying experimental cell biology, necessitating precise measurement and analysis techniques. Traditional approaches, which rely on geometric metrics derived from stained images, encounter obstacles stemming from both the imaging and analytical domains. Staining processes can disrupt the cells natural state and diminish accuracy due to photobleaching, while conventional analysis techniques, which categorize cells based on shape to discern pathophysiological conditions, often fail to capture the continuous and asynchronous nature of biological processes such as cell differentiation, immune responses, and cancer progression. In this work, we propose the use of quantitative phase imaging for morphological assessment due to its label-free nature. For analysis, we repurposed the genomic analysis toolbox to perform trajectory inference analysis purely based on morphology information. We applied the developed framework to study the progression of leukemia and breast cancer metastasis. Our approach revealed a clear pattern of morphological evolution tied to the diseases advancement, highlighting the efficacy of our method in identifying functionally significant shape changes where conventional techniques falter. This advancement offers a fresh perspective on analyzing cellular morphology and holds significant potential for the broader research community, enabling a deeper understanding of complex biological dynamics.

bioinformatics↗

bioRxiv2023

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of Spatially Offset Raman Spectroscopy (SORS) for non-invasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Youngs modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

bioengineering↗

bioRxiv2023

CellSNAP: A fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging

Quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. In particular, three-dimensional (3D) tomographic imaging of live cells has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw 3D tomograms is not well-developed. This work focuses on a critical, yet often underappreciated, step of the analysis pipeline, that of 3D cell segmentation from the acquired tomograms. The current method employed for such tasks is the Otsu-based 3D watershed algorithm, which works well for isolated cells; however, it is very challenging to draw boundaries when the cells are clumped. This process is also memory intensive since the processing requires computation on a 3D stack of images. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the segmentation of QPI images, which outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 seconds per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused segmentation tools. We envision our work will lead to the broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

bioinformatics↗

bioRxiv2023

FluoMALDI microscopy: matrix co-crystallization simultaneously enhances fluorescence and MALDI imaging

We report that co-crystallization of fluorophores with matrix-assisted laser desorption/ionization (MALDI) imaging matrices significantly enhances fluorophore brightness up to 79-fold, enabling the amplification of innate tissue autofluorescence. This discovery facilitates FluoMALDI, the imaging of the same biological sample by both fluorescence microscopy and MALDI imaging. Our approach combines the high spatial resolution and specific labeling capabilities of fluorescence microscopy with the inherently multiplexed, versatile imaging capabilities of MALDI imaging. This new paradigm eliminates the notion that MALDI matrices obscure and obstruct optical microscopy approaches, allowing to image the exact same cells in tissues, free of any physical changes between fluorescence and MALDI imaging, which minimizes data registration processes. Matrix-fluorophore co-crystallization also facilitates applications with insufficient fluorescence brightness. We showcase the capabilities of FluoMALDI imaging with endogenous and exogenous fluorophores and autofluorescence-based FluoMALDI of brain and kidney tissue sections. FluoMALDI will advance structural-functional microscopic imaging in cell biology, biomedicine, and pathology.

biochemistry↗

bioRxiv2023

RaMALDI: enabling simultaneous Raman and MALDI imaging of the same tissue section

Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, and specific biomolecular information, but has so far involved two separate, consecutive tissue sections for RSI and MALDI MSI, resulting in images from two separate entities with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/539107v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1829ccaorg.highwire.dtl.DTLVardef@3d018org.highwire.dtl.DTLVardef@531015org.highwire.dtl.DTLVardef@144f1a3_HPS_FORMAT_FIGEXP M_FIG C_FIG We demonstrate for the first time RaMALDI imaging, a streamlined, integrated multimodal imaging workflow of Raman spectroscopy imaging (RSI) and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI), which is performed on a single sample and uses one sample preparation protocol. RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample.

bioengineering↗

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↗