Single Cell Clonal Analysis Identifies an AID-Dependent Pathway of Plasma Cell Differentiation
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Germinal centers (GC) are microstructures where B cells that have been activated by antigen can improve the affinity of their B cell receptors and differentiate into memory B cells (MBCs) or antibody secreting plasma cells. Activation Induced Deaminase (AID) initiates antibody diversification in GCs by somatic hypermutation and class switch recombination. Here we have addressed the role of AID in the terminal differentiation of GC B cells by combining single cell transcriptome and immunoglobulin clonal analysis in a mouse model that traces AID-experienced cells. We identified 8 transcriptional clusters that include dark zone and light zone GC subsets, plasmablasts/plasma cells (PB), 4 subsets of MBCs and a novel prePB subset, which shares the strongest clonal relationships with PBs. Mice lacking AID have various alterations in the size and expression profiles of these transcriptional clusters. We find that AID deficiency leads to a reduced proportion of prePB cells and severely impairs transitions between the prePB and the PB subsets. Thus, AID shapes the differentiation fate of GC B cells by enabling PB generation from a prePB state.Keywords:
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Author(s): Cai, Wei | Advisor(s): Lo, Yu-Hwa | Abstract: One of the key challenges of biology is to understand how individual cells process information and respond to perturbations. However, most of the existing single cell analysis methods can only provide a glimpse of cell properties at specific time points and are unable to provide cell secretion and protein analysis at the single cell resolution. This thesis offers the description of a single-cell assay as well as a CO2-induced enrichment method for the analysis of single cells secretions.The single-cell assay introduced in this thesis enables the accommodation of different cellular types, allows for easy and efficient single cell loading and culturing, and is suitable for studying the efforts of in-vitro environmental factors in combination with drug screening. One salient feature of the assay is the non-invasive collection and survey of single cell secretions at different time points, producing unprecedented insight of single cell behaviors based on the biomarker signals from individual cells under given perturbations. In addition, the open-well design of the assay allows for simple collection of cells with standard tools such as pico-pipette for downstream processes in relating the single-cell secretions with gene analysis. Above all, the acquired information is quantitative. For example, measured by the number of exosomes each single cell secretes for a given time period, exosomal miRNA carried by exosomes secreted by single cell. Therefore, this single-cell assay provides a convenient, low-cost, and robust tool for quantitative, time lapsed studies of single cell properties.Another challenge for single cell secretion analysis is the limit-of-detection (LOD) and sensitivity. Thus, sample enrichment is an important step in the work flow of biosensing for disease detection and numerous biological or clinical processes. Most current techniques require devices that are tailored to specific chemical or physical characteristics of the target objects to enrich or capture them from the sample. The complexity within these devices all serve to, increase cost and may even limit the enrichment factor. Here, a technique of using a CO2 laser to drive targets towards the laser spot via mass transport without requiring any device fabrication processes or special reagents was introduced. To prove the concept, single-stranded DNA (ssDNA) has been enriched by more than 100,000-fold in less than 4 minutes. The temperature and evaporation rate profile at the enriched area are measured alongside theoretical analyses and modeling to monitor and understand the physical process. The formation of aggregates comprised of streptavidin Q-dots and biotin labeled exosomes with this method was demonstrated to show the capability of biosample detection, purification, and quantification. The method is not only simple and highly efficient, but also applicable to all types of biomolecules and bioparticles. Thereby promising a simple, cost effective and efficient solution for biological sample preparation for sensing, analytics, and diagnostics.
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Abstract Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation—the assignment of cell type or cell state to each sequenced cell—is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.
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Heterogeneity of a cell population has been considered a fundamental property of cellular system. Single cell analysis is essential to understand the variation within a heterogenous cell population; however, most existing single cell analysis methods can only provide a glimpse of cell property at specific time point, unable to provide phenotypic information varying in time. Here we review single cell analysis assays we developed to monitor single cell behaviors and cell secretions over time. The assays also provide the capability of translocating cells to another substrate for downstream analysis of selected single cells. J Nat Sci, 5(1):e547, 2019
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To understand the inhomogeneity of cells in biological systems, there is a growing demand on the capability of characterizing the properties of individual single cells. Since single-cell studies require continuous monitoring of the cell behaviors, an effective single-cell assay that can support time lapsed studies in a high throughput manner is desired. Most currently available single-cell technologies cannot provide proper environments to sustain cell growth and, proliferation of single cells and convenient, noninvasive tests of single-cell behaviors from molecular markers. Here, a highly versatile single-cell assay is presented that can accommodate different cellular types, enable easy and efficient single-cell loading and culturing, and be suitable for the study of effects of in vitro environmental factors in combination with drug screening. One salient feature of the assay is the noninvasive collection and surveying of single-cell secretions at different time points, producing unprecedented insight of single-cell behaviors based on the biomarker signals from individual cells under given perturbations. Above all, the acquired information is quantitative, for example, measured by the number of exosomes each single-cell secretes for a given time period. Therefore, our single-cell assay provides a convenient, low-cost, and enabling tool for quantitative, time lapsed studies of single-cell properties.
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Cells are basic functional components of eukaryotic organisms containing rich biological and physiological information. To investigate the nature of cells, a variety of fundamental and mechanistic studies of the cell functions, cellular metabolisms, metabolomic pathways, cell-cell interactions, and the interaction between cells and the surrounding microenvironment, to name a few, were conducted in recent years. Besides considerable achievements, cell-to-cell heterogeneity was recognized as the intrinsic property of cells, that is, individual cells possess unique biological traits different from other cells, even compared with their neighbors of the same genotype. Unfortunately, such cell-to-cell heterogeneity is masked by conventional analysis at the cell population level, which generally results in an averaged signal of all cells analyzed. Therefore, analytical methods that well appreciate the cell heterogeneity are desired to investigate cells at a higher resolution, that is, at the single cell level.
However, several major challenges exist in conducting single cell-level studies. First, the volume of a single cell is small (i.e., ~10 µm in diameter of a mammalian cell). Due to such dimension, the analyte within a single cell is extremely limited. Second, it is difficult to handle single cell samples due to the fragile nature of single cells. Third, some cellular species are prone to rapid turnover, and therefore, a departure from near-native biological microenvironment. Therefore, in this work, an integrated and multifunctional device, the T-probe, was designed and fabricated to conduct online and in situ analysis of live single cells at ambient condition in chapter 2. After the data acquisition, a comprehensive and generalized data analysis workflow was proposed to conduct single cell metabolomics and reveal the underlying biological principals beneath the complex raw data matrix in chapter 3. The initial data acquisition and the metabolomic analysis were further combined with more advanced data analysis methods such machine learning (ML) algorithms using artificial intelligence to handle big data to learn the underlying patterns of cellular metabolisms, and ultimately facilitating decision making. In chapter 4 and 5, different ML models were constructed based on the acquired datasets of single cells, and evaluated in terms of the predictive accuracy of an unknown single cell possessing drug resistance. Rapid and reliable predictions of cells possessing primary or drug-induced resistance were demonstrated using established ML models on the testing datasets, and the robustness of the model was validated through accurate predictions of cells on a different batch. Such results suggested promising potential of the ML models towards future point-of-care (POC) prognostic assays.
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Microfluidics has been widely used in single cell analysis. Current protocols allow either spread or round cells to be analyzed. However, the contribution of cell morphology to single cell analysis has not been noted. In this study, four proteins (EGFR, PTEN, pAKT, and pS6) in the EGFR signaling pathway are measured simultaneously using microfluidic image cytometry (MIC) in glioblastoma cells U87. The results show that the MIC technology can reveal different subsets of cells corresponding to the four protein expression levels no matter whether they are round or spread at the time of the measurements. However, sharper distinction is obtained from round cells, which implies that cellular heterogeneity can be better resolved with round cells during in situ protein quantification by imaging cytometry. This study calls attention to the role of cell morphology in single cell analysis. Future studies should examine whether differences in data interpretation resulting from cell morphology could reveal altered biological meanings.
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Despite recent advances in single-cell analysis techniques, the ability of single-cell analysis platforms to track specific cells that secreted cytokines remains limited. Here, we report a microfluidic droplet-based fluorescence imaging platform that can analyze single cell-secreted vascular endothelial growth factor (VEGF), an important regulator of physiological and pathological angiogenesis, to explore cellular physiological clues at the single-cell level. Two kinds of silica nanoparticle (NP)-based immunoprobes were developed, and they were bioconjugated to the membrane proteins of the probed cell surface via the bridging of secreted VEGF. Thus, an immunosandwich assay was built above the probed cell via fluorescence imaging analysis of each cell in isolated droplets. This analytical platform was used to compare the single-cell VEGF secretion ability of three cell lines (MCF-7, HeLa, and H8), which experimentally demonstrates the cellular heterogeneity of cells in secreting cytokines. The uniqueness of this method is that the single-cell assay is carried out above the cell of interest, and no additional carriers (beads or reporter cells) for capturing analytes are needed, which dramatically improves the availability of microdroplets. This single-cell analytical platform can be applied for determining other secreted cytokines at the single-cell level by changing other immune pairs, which will be an available tool for exploring single-cell metabonomics.
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Multiple methods for investigating cell invasion behavior in vitro have proven useful in exploring the mechanisms behind the epithelial-mesenchymal transition (EMT) and EMT-related tumor cell invasion, for example, by revealing that cell heterogeneity existed in EMT. However, several hypotheses and predictions regarding EMT heterogeneity have remained unproven because of the inability to quantitatively profile cell invasion at the single cell level. Here, we present a microfluidic chip that provides the capability of simultaneously investigating single cell invasion behavior, phenotypic diversity, and responsiveness to anti-invasion drugs. By assessing single cell invasion behavior in separate wells, cell–cell contacts and their corresponding interference in the invasion process could be excluded. The chip allowed for both precise quantitation of cell invasion and in situ phenotyping, such that any single cell heterogeneity could be detected and accurately quantified. This study has proven that the proposed hybrid epithelial/mesenchymal cell phenotype exists and is important in the EMT process. The invasion abilities of two cell lines were also assessed, either with or without EMT-promoting or EMT-inhibiting agents, proving that the chip can also be used to assess the effectiveness of antimetastatic agents. This study has demonstrated that the strategy of isolating single cells before studying their invasive properties is correct and that it provides an in vitro method for understanding cell heterogeneity during EMT. This approach also provides a mean of screening for anti-invasion agents that are focused on single cell invasion, a process known to be important for blood-borne metastasis to occur.
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Assuming normal distribution within cell populations has long been status quo in cellular research. With the fast development of techniques, numerous examples of heterogeneites within isogenic cultures have been unveiled, demonstrating the need of single cell analysis. Currently there are many widely used techniques for this, such as single cell RNA-sequencing, FACS and CyTOF, allowing high-troughput single-cell genotyping and medium to high troughput single cell phenotyping based on intracellular and cell surface proteins, but not secreted proteins. In this master’s thesis project the DropMap technology was utilized to develop high-throughput enzymatic activity assays on a single-cell level, revealing the heterogeneous nature of enzyme secretion and activation, even within cell lines. Around 1500 cells were screened in each assay with the microfluidic technique, providing a precise picture of the cell population and its heterogeneity. Within each assay interesting findings were discovered, such as subpopulations in shape of expression patterns, size and activity levels that could not have been detected in bulk measurements. The importance of activity measurements opposed to only secretion measurements was shown and described. The DropMap technology proved its usefulness both in terms of debug possibilities and explanations but above all its resolution and detailed output, providing a reliable platform for development of highly detailed enzymatic assays on a single cell level. (Less)
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