Molecular profiling of single organelles for quantitative analysis of cellular heterogeneity

Mitochondrial Dysfunction and Apoptosis in Brain Microvascular Endothelial Cells Following Blast Traumatic Brain Injury

Blood brain barrier (BBB) breakdown is a key driver of traumatic brain injury (TBI), contributing to prolonged neurological deficits and increased risk of death in TBI patients. Strikingly, the role of endothelium in the progression of BBB breakdown has not been sufficiently investigated, even though it constitutes the bulk of BBB structure. In the current study, we investigate TBI-induced changes in the brain endothelium at the subcellular level, particularly focusing on mitochondrial dysfunction, using a combination of confocal imaging, gene expression analysis, and molecular profiling by Raman spectrometry. Herein, we developed and applied an in-vitro blast-TBI (bTBI) model that employs an acoustic shock tube to deliver injury to cultured human brain microvascular endothelial cells (HBMVEC). We found that this injury results in aberrant expression of mitochondrial genes, as well as cytokines/ inflammasomes, and regulators of apoptosis. Furthermore, injured cells exhibit a significant increase in reactive oxygen species (ROS) and in Ca2+ levels. These changes are accompanied by overall reduction of intracellular proteins levels as well as profound transformations in mitochondrial proteome and lipidome. Finally, blast injury leads to a reduction in HBMVEC cell viability, with up to 50% of cells exhibiting signs of apoptosis following 24 h after injury. These findings led us to hypothesize that mitochondrial dysfunction in HBMVEC is a key component of BBB breakdown and TBI progression.

Label-Free Optical Spectroscopy for Early Detection of Oral Cancer

Oral cancer is the 16th most common cancer worldwide. It commonly arises from painless white or red plaques within the oral cavity. Clinical outcome is highly related to the stage when diagnosed. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Therefore, there is a need to develop a non-invasive cost-effective diagnostic technique to differentiate non-malignant and early-stage malignant lesions. Optical spectroscopy may provide an appropriate solution to facilitate early detection of these lesions. It has many advantages over traditional approaches including cost, speed, objectivity, sensitivity, painlessness, and ease-of use in clinical setting for real-time diagnosis. This review consists of a comprehensive overview of optical spectroscopy for oral cancer diagnosis, epidemiology, and recent improvements in this field for diagnostic purposes. It summarizes major developments in label-free optical spectroscopy, including Raman, fluorescence, and diffuse reflectance spectroscopy during recent years. Among the wide range of optical techniques available, we chose these three for this review because they have the ability to provide biochemical information and show great potential for real-time deep-tissue point-based in vivo analysis. This review also highlights the importance of saliva-based potential biomarkers for non-invasive early-stage diagnosis. It concludes with the discussion on the scope of development and future demands from a clinical point of view.

Advances in measuring cancer cell metabolism with subcellular resolution

Characterizing metabolism in cancer is crucial for understanding tumor biology and for developing potential therapies. Although most metabolic investigations analyze averaged metabolite levels from all cell compartments, subcellular metabolomics can provide more detailed insight into the biochemical processes associated with the disease. Methodological limitations have historically prevented the wider application of subcellular metabolomics in cancer research. Recently, however, ways to distinguish and identify metabolic pathways within organelles have been developed, including state-of-the-art methods to monitor metabolism in situ (such as mass spectrometry-based imaging, Raman spectroscopy and fluorescence microscopy), to isolate key organelles via new approaches and to use tailored isotope-tracing strategies. Herein, we examine the advantages and limitations of these developments and look to the future of this field of research.

Bacterial Resuscitation from Starvation-Induced Dormancy Results in Phenotypic Diversity Coupled with Translational Activity Depending on Carbon Substrate Availability

Dormancy is a survival strategy of stressed bacteria inhabiting a various environment. Frequent dormant-active transitions owing to environmental changes play an important role in functional redundancy. However, a proper understanding of the phenotypic changes in bacteria during these transitions remains to be clarified. In this study, orthogonal approaches, such as electron microscopy, flow cytometry, and Raman spectroscopy, which can evaluate phenotypic heterogeneity at the single-cell level, were used to observe morphological and molecular phenotypic changes in resuscitated cells, and RNA sequencing (RNASeq) was used to determine the genetic characteristics associated with phenotypes. Within 12 h of the resuscitation process, morphological (cell size and shape) and physiological (growth and viability) characteristics as well as molecular phenotypes (cellular components) were found to be recovered to the extent that they were similar to those in active cells. The recovery rate and detailed phenotypic properties of the resuscitated cells differed significantly depending on the type or concentration of carbon sources. RNASeq analysis revealed that genes related to translation were significantly upregulated under all resuscitation conditions. The simpler the carbon source (e.g., glucose), the higher the expression of genes involved in cellular repair, and the more complex the carbon source (e.g., beef extract), the higher the expression of genes associated with increased energy production associated with cellular aerobic respiration. This study of phenotypic plasticity of resuscitated cells provides fundamental insight into understanding the adaptive fine-tuning of the microbiome in response to environmental changes and the functional redundancy resulting from phenotype heterogeneity.

Mitochondrial Dysfunction: A Prelude to Neuropathogenesis of SARS-CoV-2

The SARS-CoV-2 virus is notorious for its neuroinvasive capability, causing multiple neurological conditions. The neuropathology of SARS-CoV-2 is increasingly attributed to mitochondrial dysfunction of brain microglia cells. However, the changes in biochemical content of mitochondria that drive the progression of neuro-COVID remain poorly understood. Here we introduce a Raman microspectrometry approach that enables the molecular profiling of single cellular organelles to characterize the mitochondrial molecular makeup in the infected microglia cells. We found that microglia treated with either spike protein or heat-inactivated SARS-CoV-2 trigger a dramatic reduction in mtDNA content and an increase in phospholipid saturation levels. At the same time, no significant changes were detected in Golgi apparatus and in lipid droplets, the organelles that accommodate biogenesis and storage of lipids. We hypothesize that transformations in mitochondria are caused by increased synthesis of reactive oxygen species in these organelles. Our findings call for the development of mitochondria-targeted therapeutic approaches to limit neuropathology associated with SARS-CoV-2.

A Single-Organelle Optical Omics Platform for Cell Science and Biomarker Discovery

Research in fundamental cell biology and pathology could be revolutionized by developing the capacity for quantitative molecular analysis of subcellular structures. To that end, we introduce the Ramanomics platform, based on confocal Raman microspectrometry coupled to a biomolecular component analysis algorithm, which together enable us to molecularly profile single organelles in a live-cell environment. This emerging omics approach categorizes the entire molecular makeup of a sample into about a dozen of general classes and subclasses of biomolecules and quantifies their amounts in submicrometer volumes. A major contribution of our study is an attempt to bridge Raman spectrometry with big-data analysis in order to identify complex patterns of biomolecules in a single cellular organelle and leverage discovery of disease biomarkers. Our data reveal significant variations in organellar composition between different cell lines. We also demonstrate the merits of Ramanomics for identifying diseased cells by using prostate cancer as an example. We report large-scale molecular transformations in the mitochondria, Golgi apparatus, and endoplasmic reticulum that accompany the development of prostate cancer. Based on these findings, we propose that Ramanomics datasets in distinct organelles constitute signatures of cellular metabolism in healthy and diseased states.

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

Metabolic reprogramming is a common hallmark in cancer. The high complexity and heterogeneity in cancer render it challenging for scientists to study cancer metabolism. Despite the recent advances in single-cell metabolomics based on mass spectrometry, the analysis of metabolites is still a destructive process, thus limiting in vivo investigations. Being label-free and nonperturbative, Raman spectroscopy offers intrinsic information for elucidating active biochemical processes at subcellular level. This review summarizes recent applications of Raman-based techniques, including spontaneous Raman spectroscopy and imaging, coherent Raman imaging, and Raman-stable isotope probing, in contribution to the molecular understanding of the complex biological processes in the disease. In addition, this review discusses possible future directions of Raman-based technologies in cancer research.

Single-cell transcriptome conservation in a comparative analysis of fresh and cryopreserved human skin tissue: pilot in localized scleroderma

Background

The purpose of this study was to assess variability in cell composition and cell-specific gene expression in the skin of patients with localized scleroderma (LS) utilizing CryoStor® CS10 in comparison to RPMI to produce adequate preservation of tissue samples and cell types of interest for use in large-scale multi-institutional collaborations studying localized scleroderma and other skin disorders.

Methods

We performed single-cell RNA sequencing on paired skin biopsy specimens from 3 patients with LS. Each patient with one sample cryopreserved in CryoStor® CS10 and one fresh in RPMI media using 10× Genomics sequencing.

Results

Levels of cell viability and yield were comparable between CryoStor® CS10 (frozen) and RPMI (fresh) preserved cells. Furthermore, gene expression between preservation methods was collectively significantly correlated and conserved across all 18 identified cell cluster populations.

Conclusion

Comparable cell population and transcript expression yields between CryoStor® CS10 and RPMI preserved cells support the utilization of cryopreserved skin tissue in single-cell analysis. This suggests that employing standardized cryopreservation protocols for the skin tissue will help facilitate multi-site collaborations looking to identify mechanisms of disease in disorders characterized by cutaneous pathology.

Raman Microscopy: Progress in Research on Cancer Cell Sensing

In the last decade, Raman Spectroscopy (RS) was demonstrated to be a label-free, non-invasive and non-destructive optical spectroscopy allowing the improvement in diagnostic accuracy in cancer and analytical assessment for cell sensing. This review discusses how Raman spectra can lead to a deeper molecular understanding of the biochemical changes in cancer cells in comparison to non-cancer cells, analyzing two key examples, leukemia and breast cancer. The reported Raman results provide information on cancer progression and allow the identification, classification, and follow-up after chemotherapy treatments of the cancer cells from the liquid biopsy. The key obstacles for RS applications in cancer cell diagnosis, including quality, objectivity, number of cells and velocity of the analysis, are considered. The use of multivariant analysis, such as principal component analysis (PCA) and linear discriminate analysis (LDA), for an automatic and objective assessment without any specialized knowledge of spectroscopy is presented. Raman imaging for cancer cell mapping is shown and its advantages for routine clinical pathology practice and live cell imaging, compared to single-point spectral analysis, are debated. Additionally, the combination of RS with microfluidic devices and high-throughput screening for improving the velocity and the number of cells analyzed are also discussed. Finally, the combination of the Raman microscopy (RM) with other imaging modalities, for complete visualization and characterization of the cells, is described.

High resolution mapping of subcellular refractive index by Fluorescence Lifetime Imaging: a next frontier in quantitative cell science?

Intracellular refractive index (RI) is an essential biophysical parameter, which best represents the mass and the distribution of proteins in the cell interior, including high-density accumulations in membraneless organelles. For RI measurements, a number of sophisticated techniques have been developed; however most of the new approaches are either insufficiently sensitive to intracellular variations of proteins distribution or are not compatible with live cell studies. Here, we outline the fluorescence lifetime imaging (FLIM) strategy for high resolution mapping of subcellular RI. We provide an example of our recent studies in which we utilize FLIM for measurements and monitoring of local RI in the major membraneless organelles within live cultured cells.

Different Grp94 components interact transiently with the myocilin olfactomedin domain in vitro to enhance or retard its amyloid aggregation

The inherited form of open angle glaucoma arises due to a toxic gain-of-function intracellular misfolding event involving a mutated myocilin olfactomedin domain (OLF). Mutant myocilin is recognized by the endoplasmic reticulum (ER)-resident heat shock protein 90 paralog, glucose regulated protein 94 (Grp94), but their co-aggregation precludes mutant myocilin clearance by ER-associated degradation. When the Grp94-mutant myocilin interaction is abrogated by inhibitors or siRNA, mutant myocilin is efficiently degraded. Here we dissected Grp94 into component domains (N, NM, MC) to better understand the molecular factors governing its interaction with OLF. We show that the Grp94 N-terminal nucleotide-binding N domain is responsible for accelerating OLF aggregation in vitro. Upon inhibiting the isolated N domain pharmacologically or removing the Pre-N terminal 57 residues from full-length Grp94, OLF aggregation rates revert to those seen for OLF alone, but only pharmacological inhibition rescues co-aggregation. The Grp94-OLF interaction is below the detection limit of fluorescence polarization measurements, but chemical crosslinking paired with mass spectrometry analyses traps a reproducible interaction between OLF and the Grp94 N domain, as well as between OLF and the Grp94 M domain. The emerging molecular-level picture of quinary interactions between Grp94 and myocilin points to a role for the far N-terminal sequence of the Grp94 N domain and a cleft in the M domain. Our work further supports drug discovery efforts to inhibit these interactions as a strategy to treat myocilin-associated glaucoma.

Inkjet-printed micro-calibration standards for ultraquantitative Raman spectral cytometry

Herein we report the development of a cytometric analysis platform for measuring the contents of individual cells in absolute (picogram) scales; this study represents the first report of Raman-based quantitation of the absolute mass - or the total amount - of multiple endogenous biomolecules within single-cells. To enable ultraquantitative calibration, we engineered single-cell-sized micro-calibration standards of known composition by inkjet-printer deposition of biomolecular components in microarrays across the surface of silicon chips. We demonstrate clinical feasibility by characterizing the compositional phenotype of human skin fibroblast and porcine alveolar macrophage cell populations in the respective contexts of Niemann-Pick disease and drug-induced phospholipidosis: two types of lipid storage disorders. We envision this microanalytical platform as the foundation for many future biomedical applications, ranging from diagnostic assays to pathological analysis to advanced pharmaco/toxicokinetic research studies.

Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning

CD8+ T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifying activated CD8+ T cells are laborious, time-consuming and expensive due to the extensive list of required reagents. Here, we demonstrate an optical imaging approach featuring quantitative phase imaging to distinguish activated CD8+ T cells from naı̈ve CD8+ T cells in a rapid and reagent-free manner. We measured the dry mass of live cells and employed transport-based morphometry to better understand their differential morphological attributes. Our results reveal that, upon activation, the dry cell mass of T cells increases significantly in comparison to that of unstimulated cells. By employing deep learning formalism, we are able to accurately predict the population ratios of unknown mixed population based on the acquired quantitative phase images. We envision that, with further refinement, this label-free method of T cell phenotyping will lead to a rapid and cost-effective platform for assaying T cell responses to candidate antigens in the near future.

The Impact of Heterogeneity on Single-Cell Sequencing

The importance of diversity and cellular specialization is clear for many reasons, from population-level diversification, to improved resiliency to unforeseen stresses, to unique functions within metazoan organisms during development and differentiation. However, the level of cellular heterogeneity is just now becoming clear through the integration of genome-wide analyses and more cost effective Next Generation Sequencing (NGS). With easy access to single-cell NGS (scNGS), new opportunities exist to examine different levels of gene expression and somatic mutational heterogeneity, but these assays can generate yottabyte scale data. Here, we model the importance of heterogeneity for large-scale analysis of scNGS data, with a focus on the utilization in oncology and other diseases, providing a guide to aid in sample size and experimental design.

Cycles of protein condensation and discharge in nuclear organelles studied by fluorescence lifetime imaging

Nuclear organelles are viscous droplets, created by concentration-dependent condensation and liquid–liquid phase separation of soluble proteins. Nuclear organelles have been actively investigated for their role in cellular regulation and disease. However, these studies are highly challenging to perform in live cells, and therefore, their physico-chemical properties are still poorly understood. In this study, we describe a fluorescence lifetime imaging approach for real-time monitoring of protein condensation in nuclear organelles of live cultured cells. This approach unravels surprisingly large cyclic changes in concentration of proteins in major nuclear organelles including nucleoli, nuclear speckles, Cajal bodies, as well as in the clusters of heterochromatin. Remarkably, protein concentration changes are synchronous for different organelles of the same cells. We propose a molecular mechanism responsible for synchronous accumulations of proteins in the nuclear organelles. This mechanism can serve for general regulation of cellular metabolism and contribute to coordination of gene expression.

Studying the condensation of proteins into membraneless organelles in live cells is highly challenging. Here the authors develop a fluorescence lifetime imaging approach to monitor the condensation of proteins in nuclear organelles and report coordinated and cyclic changes in several nuclear organelles.

Detection of Metabolic Changes Induced via Drug Treatments in Live Cancer Cells and Tissue Using Raman Imaging Microscopy

Isocitrate dehydrogenase 1 (IDH1) mutations in gliomas, fibrosarcoma, and other cancers leads to a novel metabolite, D-2-hydroxyglutarate, which is proposed to cause tumorigenesis. The production of this metabolite also causes vulnerabilities in cellular metabolism, such as lowering NADPH levels. To exploit this vulnerability, we treated glioma and fibrosarcoma cells that harbor an IDH1 mutation with an inhibitor of nicotinamide adenine dinucleotide (NAD⁺) salvage pathway, FK866, and observed decreased viability in these cells. To understand the mechanism of action by which the inhibitor FK866 works, we used Raman imaging microscopy and identified that proteins and lipids are decreased upon treatment with the drug. Raman imaging showed a different distribution of lipids throughout the cell in the presence of the drug compared with the untreated cells. We employed nuclear magnetic resonance NMR spectroscopy and mass spectrometry to identify the classes of lipids altered. Our combined analyses point to a decrease in cell division due to loss of lipid content that contributes to membrane formation in the in vitro setting. However, the FK866 drug did not have the same potency in vivo. The use of Raman imaging microscopy indicated an opposite trend of lipid distribution in the tissue collected from treated versus untreated mice when compared with the cells. These results demonstrate the role of Raman imaging microscopy to identify and quantify metabolic changes in cancer cells and tissue.

Biomolecular Component Analysis of Phospholipids Composition in Live HeLa Cells

The alteration of the phospholipid composition within the cell, in particular the ratio between saturated and unsaturated fatty acids, can serve as an important biomarker to prognosis of the disease progression (e.g., fatty-liver disease, prostate cancer, or neurodegenerative disorders). Major techniques for lipid analysis in biological samples require a lipid extraction procedure that is not compatible with live cell studies. To address this challenge, we apply microRaman-Biomolecular Component Analysis (BCA) for comparative analysis of phospholipid composition and sensing the saturation degree of fatty acid lipid chain in live HeLa cells and lipids extracted from HeLa cells. After processing raw Raman data, acquired in lipid droplets (LDs) free cytoplasmic area, LDs and extracted lipids with BCA, the lipid component was isolated. Despite the similarity in general profiles of processed Raman spectra acquired in live cells and extracted lipids, some clear differences that reflect diversity in their phospholipids composition were revealed. Furthermore, using the direct relation between the number of double bonds in the fatty acid chain and the intensity ratio of the corresponding Raman bands, the saturation degree of fatty acids was estimated.

BCAbox Algorithm Expands Capabilities of Raman Microscope for Single Organelles Assessment

Raman microspectroscopy is a rapidly developing technique, which has an unparalleled potential for in situ proteomics, lipidomics, and metabolomics, due to its remarkable capability to analyze the molecular composition of live cells and single cellular organelles. However, the scope of Raman spectroscopy for bio-applications is limited by a lack of software tools for express-analysis of biomolecular composition based on Raman spectra. In this study, we have developed the first software toolbox for immediate analysis of intracellular Raman spectra using a powerful biomolecular component analysis (BCA) algorithm. Our software could be easily integrated with commercial Raman spectroscopy instrumentation, and serve for precise analysis of molecular content in major cellular organelles, including nucleoli, endoplasmic reticulum, Golgi apparatus, and mitochondria of either live or fixed cells. The proposed software may be applied in broad directions of cell science, and serve for further advancement and standardization of Raman spectroscopy.

An Expandable Mechanopharmaceutical Device (3): a Versatile Raman Spectral Cytometry Approach to Study the Drug Cargo Capacity of Individual Macrophages

PURPOSE

To improve cytometric phenotyping abilities and better understand cell populations with high interindividual variability, a novel Raman-based microanalysis was developed to characterize macrophages on the basis of chemical composition, specifically to measure and characterize intracellular drug distribution and phase separation in relation to endogenous cellular biomolecules.

METHODS

The microanalysis was developed for the commercially-available WiTec alpha300R confocal Raman microscope. Alveolar macrophages were isolated and incubated in the presence of pharmaceutical compounds nilotinib, chloroquine, or etravirine. A Raman data processing algorithm was specifically developed to acquire the Raman signals emitted from single-cells and calculate the signal contributions from each of the major molecular components present in cell samples.

RESULTS

Our methodology enabled analysis of the most abundant biochemicals present in typical eukaryotic cells and clearly identified "foamy" lipid-laden macrophages throughout cell populations, indicating feasibility for cellular lipid content analysis in the context of different diseases. Single-cell imaging revealed differences in intracellular distribution behavior for each drug; nilotinib underwent phase separation and self-aggregation while chloroquine and etravirine accumulated primarily via lipid partitioning.

CONCLUSIONS

This methodology establishes a versatile cytometric analysis of drug cargo loading in macrophages requiring small numbers of cells with foreseeable applications in toxicology, disease pathology, and drug discovery.

Ramanomics: New Omics Disciplines Using Micro Raman Spectrometry with Biomolecular Component Analysis for Molecular Profiling of Biological Structures

Modern instrumentation for Raman microspectroscopy and current techniques in analysis of spectral data provide new opportunities to study molecular interactions and dynamics at subcellular levels in biological systems. Implementation of biomolecular component analysis (BCA) to microRaman spectrometry provides basis for the emergence of Ramanomics, a new biosensing discipline with unprecedented capabilities to measure concentrations of distinct biomolecular groups in live cells and organelles. Here we review the combined use of microRaman-BCA techniques to probe absolute concentrations of proteins, DNA, RNA and lipids in single organelles of live cells. Assessing biomolecular concentration profiles of organelles at the single cell level provides a physiologically relevant set of biomarkers for cellular heterogeneity. In addition, changes to an organelle's biomolecular concentration profile during a cellular transformation, whether natural, drug induced or disease manifested, can provide molecular insight into the nature of the cellular process.