Single cell metabolism: current and future trends.

Microfluidic-based single cells analysis has been of great interest in recent years, promising disease diagnosis and personalized medicine. Current technologies are challenging in bioselectively isolating specific single cells from complex matrices. Herein, a novel microfluidic platform integrated with cell-recognizable aptamer-encoded microwells was specifically developed to isolate single tumor cells with satisfied single-cell occupancy and unique bioselectivity. In this work, the designed microwell-structures enable us to encourage strong 3D local topographic interactions of the target cell surface with biomolecules and regulate the single-cell resolution. Under the optimized size of microwells, the single-cell occupancy was significantly enhanced from 0.5% to 88.2% through the introduction of the aptamer. Analysis of the target cells was directly performed in short time periods (<5.0 min) with small volumes (4.5 μL). Importantly, such an aptamer-enabled microfluidic device shows an excellent selectivity for target single cells isolation compared with three control cells. Subsequently, targeted isolation and analysis of single tumor cells were demonstrated by using artificial complex cell samples at simulated conditions, and various cellular carboxylesterases were studied by time-course measurements of cellular fluorescence kinetics at individual-cell level. Thus, our technique will open up a new opportunity in single-cell level-based disease diagnosis and personalize medicine screening.

Pancreatic Cancer Ductal Adenocarcinoma (PDAC) is one of the deadliest cancers with 5-year survival of 11%. Understanding the intratumor heterogeneity is a pivotal piece to unravel the complexity of PDAC. While single cell RNASeq identifies the heterogeneous cell populations within the tumor tissue, spatially characterizing the transcriptomic profile of neoplastic and pre-neoplastic populations within the tissue remains a challenge, as the spatial dimension is usually lost upon tissue dissociation. We identified an approach to integrate spatial transcriptomics data with single cell RNASeq data. To characterize the cell populations within the tissue we performed single cell RNASeq on disease pathology-free pancreas tissue and primary PDAC samples. We profiled the transcriptomic profile of Acinar, Ductal, Acinar-to-Ductal (ADM), and Pancreatic Intraepithelial Neoplasia (PanINs) regions of interest (ROIs) across the tissue using the Nanostring GeoMx platform. Differential gene expression analysis using linear mixed-effect models of the cell-type specific ROIs defined pan-marker gene sets for each cell type, which were mapped to UMAP projections of single cell RNA sequencing data using AUCell scoring. As expected, the acinar pan-markers gene set derived from the spatial transcriptomics mapped to the manually annotated acinar population in the single cell data. On the other hand, Ductal, ADM, and PanIN pan-marker gene sets were mapped to distinct clusters that previously were not well-defined by single cell sequencing. The analysis coupled with orthogonal validation using RNAScope revealed gene signatures uniquely specific to ADM lesions and PanINs, respectively. Interestingly, our list included known markers as well as novel findings, supporting the validity of the findings. Furthermore, RNA velocity analysis using scVelo revealed a trajectory of cell evolution originating from acinar cells passing through the newly-defined ADM population and ending towards the ductal population derived from tumor samples. Overall, this integration approach of spatial and single cell transcriptomics can further define the characteristics that differentiate neoplastic and pre-neoplastic populations, as well as the potential drivers for tumorigenesis that could be therapeutically targeted. Citation Format: Ahmed M. Elhossiny, Eileen Carpenter, Padma Kadiyala, Yaqing Zhang, Filip Bednar, Arvind Rao, Timothy Frankel, Marina Pasca Di Magliano. Integrating single cell and spatial transcriptomics define gene signature for pancreatic ductal adenocarcinoma pre-neoplastic lesion [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A006.

Recent advances in the development of single cell analysis—A review

Single-cell proteomics in complex tissues using microprobe capillary electrophoresis mass spectrometry.

Tumor heterogeneity represents a major obstacle to effective cancer treatment and personalized medicine. Hepatocellular carcinoma (HCC) is clinically and biologically heterogeneous, which is partly attributed to the presence of hepatic cancer stem cells (CSCs). CSC surface makers can have overlapping expression or exclusive expression of different subpopulations of hepatic CSCs, and these cells may contain different oncogenic changes. The existence of various CSC surface-marked subpopulations might pose a problem for CSC targeted therapeutics. Little is known about whether there are shared or distinct pathways in different subpopulations. Moreover, it remains unknown whether heterogeneity exists within certain subpopulations. Thus, we propose to profile the global transcriptome of surface-marked CSCs (EpCAM+, CD133+ and CD24+) at the single-cell level using index flow cytometric sorting and single-cell RNA sequencing technology. We statistically compared mRNA transcriptomes of individual single cells of triple positive (EpCAM+/ CD133+ /CD24+) CSCs and triple negative (EpCAM-/ CD133- /CD24-) cells. Further, we compared the triple positive and the triple negative cells at the single cell level in terms of their self-renew capability in both normoxic and hypoxic conditions. Our results show that at the single cell level, the transcriptomic profiles show a high-degree of heterogeneity in HCC cell lines, meanwhile, there is a dramatic difference between triple positive and triple negative cells in their transcriptomic profiles. There is a high-degree of intra-tumor and inter-tumor heterogeneity observed in single cell transcriptomes of HCC patients’ samples. Triple positive CSC single cells show continuous rather than discrete stemness-related gene expression patterns. In parallel, the self-renewal capability of triple positive cells also exhibits heterogeneity, while triple negative cells show little self-renewal capability. Thus, our single cell analysis study reveals molecular and biological heterogeneity of cancer stem cells in HCC, which may provide insight into the underlying mechanisms of how CSCs contribute to tumor heterogeneity and underscore key pathways and novel targets for hepatic CSC therapy. Citation Format: Hongping Zheng, Yotsawat Pomyen, Maria Hernandez, Caiyi Li, Ferenc Livak, Tim Greten, Anuradha Budhu, Xin Wang. Single cell analysis reveals cancer stem cell heterogeneities in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 936. doi:10.1158/1538-7445.AM2017-936

Techniques that allow single cell analysis are gaining widespread attention, and most of these studies utilize genomics-based approaches. While nanofluidic technologies have enabled mass spectrometric analysis of single cells, these measurements have been limited to metabolomics and lipidomic studies. Single cell proteomics has the potential to improve our understanding of intercellular heterogeneity. However, this approach has faced challenges including limited sample availability, as well as a requirement of highly sensitive methods for sample collection, cleanup, and detection. We present a technique to overcome these limitations by combining a micropipette (pulled glass capillary) based sample collection strategy with offline sample preparation and nanoLC-MS/MS to analyze proteins through a bottom-up proteomic strategy. This study explores two types of proteomics data acquisition strategies namely data-dependent (DDA) and data-independent acquisition (DIA). Results from the study indicate DIA to be more sensitive enabling analysis of >1600 proteins from ∼130 μm Xenopus laevis embryonic cells containing <6 nL of cytoplasm. The method was found to be robust in obtaining reproducible protein quantifications from single cells spanning the 1-128-cell stages of development. Furthermore, we used micropipette sampling to study intercellular heterogeneity within cells in a single embryo and investigated embryonic asymmetry along both animal-vegetal and dorsal-ventral axes during early stages of development. Investigation of the animal-vegetal axis led to discovery of various asymmetrically distributed proteins along the animal-vegetal axis. We have further compared the hits found from our proteomic data sets with other studies and validated a few hits using an orthogonal imaging technique. This study forms the first report of vegetal enrichment of the germ plasm associated protein DDX4/VASA in Xenopus embyos. Overall, the method and data presented here holds promise to enable important leads in developmental biology.

BackgroundTherapeutic decisions in cancer are generally guided by molecular biomarkers or, for some newer therapeutics, primary tumor genotype. However, because biomarkers or genotypes may change as new metastases emerge, circulating tumor cells (CTCs) from blood are being investigated for a role in guiding real-time drug selection during disease progression, expecting that CTCs will comprehensively represent the full spectrum of genomic changes in metastases. However, information is limited regarding mutational heterogeneity among CTCs and metastases in breast cancer as discerned by single cell analysis. The presence of disseminated tumor cells (DTCs) in bone marrow also carry prognostic significance in breast cancer, but with variability between CTC and DTC detection. Here we analyze a series of single tumor cells, CTCs, and DTCs for PIK3CA mutations and report CTC and corresponding metastatic genotypes.MethodsWe used the MagSweeper, an immunomagnetic separation device, to capture live single tumor cells from breast cancer patients’ primary and metastatic tissues, blood, and bone marrow. Single cells were screened for mutations in exons 9 and 20 of the PIK3CA gene. Captured DTCs grown in cell culture were also sequenced for PIK3CA mutations.ResultsAmong 242 individual tumor cells isolated from 17 patients and tested for mutations, 48 mutated tumor cells were identified in three patients. Single cell analyses revealed mutational heterogeneity among CTCs and tumor cells in tissues. In a patient followed serially, there was mutational discordance between CTCs, DTCs, and metastases, and among CTCs isolated at different time points. DTCs from this patient propagated in vitro contained a PIK3CA mutation, which was maintained despite morphological changes during 21 days of cell culture.ConclusionsSingle cell analysis of CTCs can demonstrate genotypic heterogeneity, changes over time, and discordance from DTCs and distant metastases. We present a cautionary case showing that CTCs from any single blood draw do not always reflect metastatic genotype, and that CTC and DTC analyses may provide independent clinical information. Isolated DTCs remain viable and can be propagated in culture while maintaining their original mutational status, potentially serving as a future resource for investigating new drug therapies.

Metal/Matrix Enhanced Time-of-flight Secondary Ion Mass Spectrometry for Single Cell Lipids Analysis

The quantification of elemental concentration in cells is usually performed by analytical assays on large populations missing peculiar but important rare cells. The present article aims at comparing the elemental quantification in single cells and cell population in three different cell types using a new approach for single cells elemental analysis performed at sub-micrometer scale combining X-ray fluorescence microscopy and atomic force microscopy. The attention is focused on the light element Mg, exploiting the opportunity to compare the single cell quantification to the cell population analysis carried out by a highly Mg-selective fluorescent chemosensor. The results show that the single cell analysis reveals the same Mg differences found in large population of the different cell strains studied. However, in one of the cell strains, single cell analysis reveals two cells with an exceptionally high intracellular Mg content compared with the other cells of the same strain. The single cell analysis allows mapping Mg and other light elements in whole cells at sub-micrometer scale. A detailed intensity correlation analysis on the two cells with the highest Mg content reveals that Mg subcellular localization correlates with oxygen in a different fashion with respect the other sister cells of the same strain. Graphical abstract Single cells or large population analysis this is the question!

Our understanding of the diversity of cells that escape the primary tumor and seed micrometastases remains rudimentary, and approaches for studying circulating and disseminated tumor cells have been limited by low throughput and sensitivity, reliance on single parameter sorting, and a focus on enumeration rather than phenotypic and genetic characterization. Here, we utilize a highly sensitive microfluidic and dielectrophoretic approach for the isolation and genetic analysis of individual tumor cells. We employed fluorescence labeling to isolate 208 single cells from spiking experiments conducted with 11 cell lines, including 8 neuroblastoma cell lines, and achieved a capture sensitivity of 1 tumor cell per 106 white blood cells (WBCs). Sample fixation or freezing had no detectable effect on cell capture. Point mutations were accurately detected in the whole genome amplification product of captured single tumor cells but not in negative control WBCs. We applied this approach to capture 144 single tumor cells from 10 bone marrow samples of patients suffering from neuroblastoma. In this pediatric malignancy, high-risk patients often exhibit wide-spread hematogenous metastasis, but access to primary tumor can be difficult or impossible. Here, we used flow-based sorting to pre-enrich samples with tumor involvement below 0.02%. For all patients for whom a mutation in the Anaplastic Lymphoma Kinase gene had already been detected in their primary tumor, the same mutation was detected in single cells from their marrow. These findings demonstrate a novel, non-invasive, and adaptable method for the capture and genetic analysis of single tumor cells from cancer patients.

Single cell analysis is an essential research approach to reveal the secret of life. At such a small scale, it puts forward higher demand on the accuracy of the cell manipulation system. This paper reviews the state of the art of micro-nanorobotic manipulation in single cell analysis. First, the key applications of the micro-nanorobotic manipulation system in single cell analysis are introduced, including the single cell injection, positioning, characterization, assembly, and the development of biomedical device. Then, the current key techniques, challenges, and future trends in micro-nanorobot are discussed from the aspects of actuating, sensing, controlling, system integration, and commercialization. To meet the requirement of cell biology, the next generation micro-nanorobot should have small, automatic, high integration and applicable features in multiscale.

Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection (e.g., our recent boosting to amplify signal with isobaric labeling (BASIL) approach) has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have systematically evaluated and optimized the BASIL approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of single cells. This improved, intelligent BASIL (iBASIL) approach enables comprehensive and quantitative single-cell proteomics analysis by carefully controlling the boosting-to-sample ratio and optimizing MS automatic gain control and ion injection time settings. Using standard LC-MS platforms, iBASIL enabled precise quantification of 800-1,200 proteins from single FACS-isolated cells (depending on the cell type). Moreover, coupling iBASIL to nanoscale fractionation enabled identification of &amp;gt;3,000 proteins and reliable quantification of &amp;gt;2,000 proteins while readily separating single cells from 3 different acute myeloid leukemia cell lines. We believe iBASIL has broad utility for comprehensive and precise quantitative single-cell proteomics for systems biology and biomedical research, as well as for comprehensive proteomic analysis of size-limited clinical specimens that cannot be readily accessed by regular proteomics platforms. It also has the potential to be adapted for phosphoproteome analysis of as few as 100 cells. Citation Format: Chia-Feng Tsai, Rui Zhao, Ronald Moore, Sarah Williams, Kendall Schultz, Paul Piehowski, Ljiljana Pasa-Tolic, Karin Rodland, Richard Smith, Tujin Shi, Ying Zhu, Ying Zhu, Tao Liu. An intelligent boosting to amplify signal with isobaric labeling (iBASIL) strategy for precise quantitative single-cell proteomics analysis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2856.

Single-Cell Imaging

Single cell analytics allows quantitative investigation of single biological cells from a structural, functional and proteomics point of view and opens possibilities to a novel unamplified cell analysis inherently insensitive to ensemble-averaging, cell-cycle or cell-population effects. We report on three different experimental methods and their application to cellular systems with single molecule sensitivity at the single cell level. Firstly, atomic force microscopy (AFM) can be used to elucidate the surface structure of living bacteria down to the nanometer scale where identification of irregular surface areas and 2D-arrays of regular protein s-layers is possible. Secondly, single cell manipulation and probing experiments with optical tweezers (OT) force spectroscopy allows quantitative identification of individual recognition events of membrane bound receptors. And thirdly, a novel, single cell analysis for protein fingerprinting in structured microfluidic device format will allow a future (label-free) on-chip electrophoretical protein separation of single cells without preamplification.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning