Single-cell Analysis Approaches Research Articles

Abstract 3709: Single cell analysis from CSF of patients with suspected leptomeningeal metastasis

Abstract Introduction: Leptomeningeal metastases (LM) are diagnosed in up to 10% of metastatic cancer patients, the most frequent cancers being carcinomas of breast and lung or melanoma. Due to a dismal prognosis and an overall median survival of only four months new approaches for molecular analysis could help to identify individual treatment options. Material and Methods: In this study, we collected 28 cerebrospinal fluid (CSF) samples from patients with different primary tumors but suspected LM. CSF was divided in two parts for standard neuropathologic cytomorphology and detection of circulating tumor cells (CTCs) by using the CellSearch® CTC Kit, respectively. Subsequently, single Cytokeratin-positive CTCs were isolated for whole genome amplification by Ampli1TM and molecular analysis. Additionally, we were able to isolate cell-free DNA (cfDNA) from CSF in a subset of patients which we amplified by an adapted Ampli1TM library protocol. In a small subset of patients, we collected slices of formalin-fixed paraffin embedded (FFPE) primary tumors and systemic metastases for dissociation and isolation of pure subpopulations by DEPArrayTM technology. Isolated CTCs, cfDNA and subpopulations of tumor and stromal cells were analyzed for copy number variations (CNV) applying the Ampli1TM LowPass protocol on the Illumina MiSeqTM platform. Results: By comparing results from classical cytomorphology and CellSearch® workflow, we could find high concordance (24/28 samples) with a slightly higher tumor cell detection rate by CellSearch® (11 positive cases vs. 9 by cytomorphology). Importantly, we could confirm the malignant origin of isolated cells by CNV analysis in all patients positive in CellSearch® assay. Moreover, by comparing single cell data with cfDNA analysis of a subset of 20 samples, we could also detect a higher sensitivity for tumor detection in CTC-based analysis (8/20 cases positive) in comparison to cfDNA-based analysis (5/20 cases positive). In a comprehensive analysis of multiple samples of an individual patient, including CTCs and cfDNA from CSF at different time points, CTCs from blood and biopsies from multiple visceral metastasis, we could identify divergent genomic evolution between tumor cells from the central nervous syste Conclusion: In this proof-of-concept study we show that a liquid biopsy-based approach for single cell analysis from CSF holds potential for innovative diagnostic assays for patients with suspected LM. Combining CTC quantification with molecular single cell analysis could enable us to define predictive tests to select novel treatment options for patients with LM in the future. Citation Format: Caecilia Koestler, Giancarlo Feliciello, Florian Lueke, Kathrin Weidele, Saida Zoubaa, Peter Hau, Tobias Pukrop, Markus J. Riemenschneider, Christoph A. Klein, Bernhard Polzer. Single cell analysis from CSF of patients with suspected leptomeningeal metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3709.

Computational single cell oncology: state of the art.

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Adhesion tunes speed and persistence by coordinating protrusions and extracellular matrix remodeling.

Cell migration through 3D environments is essential to development, disease, and regeneration processes. Conceptual models of migration have been developed primarily on the basis of 2D cell behaviors, but a general understanding of 3D cell migration is still lacking due to the added complexity of the extracellular matrix. Here, using a multiplexed biophysical imaging approach for single-cell analysis of human cell lines, we show how the subprocesses of adhesion, contractility, actin cytoskeletal dynamics, and matrix remodeling integrate to produce heterogeneous migration behaviors. This single-cell analysis identifies three modes of cell speed and persistence coupling, driven by distinct modes of coordination between matrix remodeling and protrusive activity. The framework that emerges establishes a predictive model linking cell trajectories to distinct subprocess coordination states.

QUAS-R: An SLC1A5-mediated glutamine uptake assay with single-cell resolution reveals metabolic heterogeneity with immune populations

System-level analysis of single-cell data is rapidly transforming the field of immunometabolism. Given the competitive demand for nutrients in immune microenvironments, there is a need to understand how and when immune cells access these nutrients. Here, we describe a new approach for single-cell analysis of nutrient uptake where we use in-cell biorthogonal labeling of a functionalized amino acid after transport into the cell. In this manner, the bona fide active uptake of glutamine via SLC1A5/ASCT2 could be quantified. We used this assay to interrogate the transport capacity of complex immune subpopulations, both invitro and invivo. Taken together, our findings provide an easy sensitive single-cell assay to assess which cells support their function via SLC1A5-mediated uptake. This is a significant addition to the single-cell metabolic toolbox required to decode the metabolic landscape of complex immune microenvironments.

ScRNA-Seq of Cultured Human Amniotic Fluid from Fetuses with Spina Bifida Reveals the Origin and Heterogeneity of the Cellular Content.

Amniotic fluid has been proposed as an easily available source of cells for numerous applications in regenerative medicine and tissue engineering. The use of amniotic fluid cells in biomedical applications necessitates their unequivocal characterization; however, the exact cellular composition of amniotic fluid and the precise tissue origins of these cells remain largely unclear. Using cells cultured from the human amniotic fluid of fetuses with spina bifida aperta and of a healthy fetus, we performed single-cell RNA sequencing to characterize the tissue origin and marker expression of cultured amniotic fluid cells at the single-cell level. Our analysis revealed nine different cell types of stromal, epithelial and immune cell phenotypes, and from various fetal tissue origins, demonstrating the heterogeneity of the cultured amniotic fluid cell population at a single-cell resolution. It also identified cell types of neural origin in amniotic fluid from fetuses with spina bifida aperta. Our data provide a comprehensive list of markers for the characterization of the various progenitor and terminally differentiated cell types in cultured amniotic fluid. This study highlights the relevance of single-cell analysis approaches for the characterization of amniotic fluid cells in order to harness their full potential in biomedical research and clinical applications.

Mass cytometry to characterize the immune lung cancer microenvironment.

The human tumor microenvironment requires use of high-dimensional single-cell tools to uncover its cellular complexity and functional variety. For decades, flow cytometry has been the technology of choice to explore immune cell diversity in different pathological contexts. Recently, a new format for flow cytometry - termed mass cytometry - has been developed. It allows for simultaneous interrogation of more than 40 different molecular markers, without the need for spectral compensation, which significantly augments the ability of cytometry to evaluate complex cellular systems and processes. Currently, different multiparametric single-cell analysis approaches are being widely adopted to interrogate the human tumor microenvironment. However, important challenges must be addressed when solid tissues are analyzed by single-cell technologies. This protocol describes the challenge and better use of single-cell mass cytometry to dissect tumor infiltrating leukocytes from surgically resected tumoral lung tissues.

Recent Advances in Single Cell Analysis by Electrochemiluminescence.

Understanding biological mechanisms operating in cells is one of the major goals of biology. Since heterogeneity is the fundamental property of cellular systems, single cell measurements can provide more accurate information about the composition, dynamics, and regulatory circuits of cells than population-averaged assays. Electrochemiluminescence (ECL), the light emission triggered by electrochemical reactions, is an emerging approach for single cell analysis. Numerous analytes, ranging from small biomolecules such as glucose and cholesterol, proteins and nucleic acids to subcellular structures, have been determined in single cells by ECL, which yields new insights into cellular functions. This review aims to provide an overview of research progress on ECL principles and systems for single cell analysis in recent years. The ECL reaction mechanisms are briefly introduced, and then the advances and representative works in ECL single cell analysis are summarized. Finally, outlooks and challenges in this field are addressed.

Isolation of single cells from human uterus in the third trimester of pregnancy: myometrium, decidua, amnion and chorion.

During pregnancy, interactions between uterine immune cells and cells of the surrounding reproductive tissues are thought to be vital for regulating labour. The mechanism that specifically initiates spontaneous labour has not been determined, but distinct changes in uterine immune cell populations and their activation status have been observed during labour at term gestation. To understand the regulation of human labour by the immune system, the ability to isolate both immune cells and non-immune cells from the uterus is required. Here, we describe protocols developed in our laboratory to isolate single cells from uterine tissues, which preserve both immune and non-immune cell populations for further analysis. We provide detailed methods for isolating immune and non-immune cells from human myometrium, chorion, amnion and decidua, together with representative flow cytometry analysis of isolated cell populations present. The protocols can be completed in tandem and take approximately 4-5 h, resulting in single-cell suspensions that contain viable leucocytes, and non-immune cells in sufficient numbers for single-cell analysis approaches such as flow cytometry and single cell RNA sequencing (scRNAseq).

Extricating human tumour immune alterations from tissue inflammation

Immunotherapies have achieved remarkable successes in the treatment of cancer, but major challenges remain1,2. An inherent weakness of current treatment approaches is that therapeutically targeted pathways are not restricted to tumours, but are also found in other tissue microenvironments, complicating treatment3,4. Despite great efforts to define inflammatory processes in the tumour microenvironment, the understanding of tumour-unique immune alterations is limited by a knowledge gap regarding the immune cell populations in inflamed human tissues. Here, in an effort to identify such tumour-enriched immune alterations, we used complementary single-cell analysis approaches to interrogate the immune infiltrate in human head and neck squamous cell carcinomas and site-matched non-malignant, inflamed tissues. Our analysis revealed a large overlap in the composition and phenotype of immune cells in tumour and inflamed tissues. Computational analysis identified tumour-enriched immune cell interactions, one of which yields a large population of regulatory T (Treg) cells that is highly enriched in the tumour and uniquely identified among all haematopoietically-derived cells in blood and tissue by co-expression of ICOS and IL-1 receptor type 1 (IL1R1). We provide evidence that these intratumoural IL1R1+ Treg cells had responded to antigen recently and demonstrate that they are clonally expanded with superior suppressive function compared with IL1R1− Treg cells. In addition to identifying extensive immunological congruence between inflamed tissues and tumours as well as tumour-specific changes with direct disease relevance, our work also provides a blueprint for extricating disease-specific changes from general inflammation-associated patterns.

TAMI-35. DETECTING SINGLE-CELL INTERACTIONS IN ORGANOTYPIC CULTURES OF GLIOBLASTOMA USING BARCODED RABIES VIRUS

Abstract Cell-cell interactions are thought to drive tumor-promoting signals in the microenvironment of glioblastoma, but standard approaches for single cell analysis do not directly identify cell interactions and the mechanisms that mediate them. We recently developed a novel method to analyze cell-cell interactions—rabies barcode interaction detection followed by sequencing (RABID-seq), which combines barcoded viral tracing and single-cell RNA sequencing (scRNAseq). RABID-seq was first implemented in transgenic mice to investigate the interactions of astrocytes with other cells in the CNS enabling the study of astrocyte connectome perturbations and candidate therapeutic targets in multiple sclerosis and its pre-clinical model, experimental autoimmune encephalomyelitis (EAE). Here, we report the first use of RABID-seq in human tissues in organotypic cultures established from three IDH-wildtype glioblastoma (GBM) patients. In organotypic GBM cultures, initial infection by pseudotyped barcoded rabies virus deficient for viral glycoprotein was achieved after previous culture transduction with a lentivirus containing the avian TVA receptor and rabies glycoprotein under the human EF1a promoter. We employed this system to initially infect approximately 1,000 malignant or non malignant cells in the tumor microenvironment. After five days, infected cells were isolated from cultures and processed for single cell analysis using SMART seq. We were able to capture at least 6,000 interacting cells per tumor specimen, from which barcodes were recovered and cDNA was sent for sequencing. Here we present connectomic data from our initial cohort of three glioblastoma patients as an introduction to RABID-seq, with a focus on astrocyte-tumor interactions. Candidate mechanisms of cellular interactions will undergo functional validation in murine models of glioblastoma.

Deep Phenotyping Reveals Expansion of T Follicular Regulatory Cells and Triple Positive (CTLA4, TIGIT, PD1) Exhausted Effector Memory T Cells Characterizing the Immunosuppressive Microenvironment in Follicular Lymphoma

INTRODUCTIONLymphoma cells are dependent on the tumor microenvironment (TME) for their survival and proliferation. Dissection of TME composition provides insight into lymphomagenesis, better prognostication, and enhancement of therapeutic options. Flow cytometry provides a robust approach for single-cell analysis. Nonetheless lack of well-defined comparator groups and/or a robust, reproducible approaches for analyzing the multidimensional data often limited such studies. In the current study, we analyzed the T cell background on a large reference set of follicular hyperplasia (FH) lymph node samples utilizing a robust standardized high dimensionality flow cytometry and a novel reproducible analytical pipeline. We then compared this baseline reference set with tumor infiltrating T cell subsets in follicular lymphoma (FL; in treatment naïve FL-NA and relapsed/refractory FL-RR sets).METHODSOn behalf of the imCORE Network we analyzed 44 FH and 81 FL (35 FL-NA and 44 FL-RR) with 2 standardized flow cytometry panels (23 antigen/18-color) (Table 1). We evaluated the T cell subset distribution as well as immune checkpoint expression (TIGIT, TIM3, PD-1, CD96, LAG3, CTLA4, CD73) within analytically defined T cell clusters. Analysis was performed using semiautomated multi-step analytical pipeline as outlined in Figure 1. Using standardized instrument settings and an R-based algorithms (gaussNorm) to minimize technical variations in sample acquisition we analyzed the T-cells subpopulations using a dimensionality reduction technique (UMAP), combined with an unsupervised clustering algorithm (FlowSOM). Statistical analysis was performed using non-parametric Wilcoxon test.RESULTSIn the 3 cohorts, several marked differences in the composition T cells subsets were observed. Compared to FH the FL lymph nodes were depleted for CD4 & CD8 naïve subsets (Table 2) and were characterized by an immune suppressive microenvironment enriched in specific subsets of activated T regulatory cells and exhausted memory effector cells. The pool of CD8 naïve cells was restored in FL-RR cases (Table 2).FL-NA nodes showed enrichment of T follicular regulatory cells (Tfr; Table 2) (0.9% vs 2.7%, p<0.0001) expressing FoxP3, dim to negative CD25, CD45RO, TIGIT, PD1, CTLA and CXCR5 (Table 2) and activated regulatory T cells (T-Reg) characterized by a highly suppressive phenotype CD25+, CTLA4+, TIGIT+ and PD1+ (Fig. 2B). Moreover, Tfr compartment was further expanded in relapsed/refractory FL cases (2.7% vs 4.5%, p=0.02).In association with the increase of Tfr cells, the concentration of CTLA4+, TIGIT+, PD1bright T follicular helper cells (Tfh) was reduced in FL-RR compared to FL-NA (Fig. 2C).The Tfr/Treg expansion was also associated with a marked increase in exhausted memory T cells in both CD4 and CD8 compartments (CD4 EM TP and CD8 EM DP, Table 2). In FL isolates the CD4 compartment was characterized by the expression of triple positive (CTLA4, TIGIT, PD1) phenotype in EM cells while the memory CD8 cells overexpressed TIGIT and PD1 (Fig. 2D). A smaller subpopulation of memory CD8 cells, almost undetectable in FH samples, characterized by the expression of CTLA4, PD1, TIGIT and TIM3 is expanded in FL isolates (Fig. 2D).CONCLUSIONOur data suggest that change in balance between TFH and Tfr may lead to more aggressive therapy resistant disease in FL. The interplay between TFH and Tfr, has been postulated to shape the immune response in FL with TFH promoting germinal center formation and Tfr inhibiting TFH and follicular effector T cells. While both TFH and Tfr compartments are expanded in FL, in the relapsed/ refractory FL cases, the Tfr compartment is further expanded at the expense of TFH leading to more immunosuppressive background. Furthermore our study suggests a rational way of designing immune checkpoint inhibitor studies in FL. Effector memory T-cells in FL isolates show an exhausted phenotype characterized by the expression of the inhibitory receptors CTLA4, TIGIT, PD1 in the CD4 compartment, and TIGIT, PD1 in the CD8. In addition, the noteworthy expansion of TIM3+ memory CD8 cells in FL. Targeting these most highly expressed checkpoints in FL alone or in combination may provide an avenue for rational trial design. [Display omitted] DisclosuresRoshal: Celgene: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services; Physicians' Education Resource: Other: Provision of services. Dogan: Physicians' Education Resource: Honoraria; Peer View: Honoraria; Roche: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Seattle Genetics: Consultancy; EUSA Pharma: Consultancy.

FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology

AbstractLarge-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge when attempting to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large data sets. It includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering, and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T-cell compartment in bone marrow (BM) with peripheral blood (PB) from patients with smoldering multiple myeloma (SMM), identify minimally invasive immune biomarkers of progression from smoldering to active MM, define prognostic T-cell subsets in the BM of patients with active MM after treatment intensification, and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation were identified in 150 patients with SMM (hazard ratio [HR], 1.7; P < .001). We also determined progression-free survival (HR, 4.09; P < .0001) and overall survival (HR, 3.12; P = .047) in 100 patients with active MM. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM and PB, and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality control, analyze high-dimensional data, unveil cellular diversity, and objectively identify biomarkers in large immune monitoring studies. These trials were registered at www.clinicaltrials.gov as #NCT01916252 and #NCT02406144.

High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery.

High throughput single cell multi-omics platforms, such as mass cytometry (cytometry by time-of-flight; CyTOF), high dimensional imaging (>6 marker; Hyperion, MIBIscope, CODEX, MACSima) and the recently evolved genomic cytometry (Citeseq or REAPseq) have enabled unprecedented insights into many biological and clinical questions, such as hematopoiesis, transplantation, cancer, and autoimmunity. In synergy with constantly adapting new single-cell analysis approaches and subsequent accumulating big data collections from these platforms, whole atlases of cell types and cellular and sub-cellular interaction networks are created. These atlases build an ideal scientific discovery environment for reference and data mining approaches, which often times reveals new cellular disease networks. In this review we will discuss how combinations and fusions of different -omic workflows on a single cell level can be used to examine cellular phenotypes, immune effector functions, and even dynamic changes, such as metabolomic state of different cells in a sample or even in a defined tissue location. We will touch on how pre-print platforms help in optimization and reproducibility of workflows, as well as community outreach. We will also shortly discuss how leveraging single cell multi-omic approaches can be used to accelerate cellular biomarker discovery during clinical trials to predict response to therapy, follow responsive cell types, and define novel druggable target pathways. Single cell proteome approaches already have changed how we explore cellular mechanism in disease and during therapy. Current challenges in the field are how we share these disruptive technologies to the scientific communities while still including new approaches, such as genomic cytometry and single cell metabolomics.

Simple-to-Operate Approach for Single Cell Analysis Using a Hydrophobic Surface and Nanosized Droplets.

Microfluidics-based technologies for single-cell analysis are becoming increasingly important tools in biological studies. With the increasing sophistication of microfluidics, cellular barcoding techniques, and next-generation sequencing, a more detailed picture of cellular subtype is emerging. Unfortunately, the majority of the methods developed for single-cell analysis are high-throughput and not suitable for rare cell analysis as they require a high input cell number. Here, we report a low-cost and reproducible method for rare single-cell analysis using a highly hydrophobic surface and nanosized static droplets. Our method allows rapid and efficient on-chip single-cell lysis and subsequent collection of genetic materials in nanoliter droplets using a micromanipulator or a laboratory pipette before subsequent genetic analysis. We show precise isolation of single cancer cells with high purity using two different strategies (i- cytospin and ii- static droplet array) for subsequent RNA analysis using droplet digital polymerase chain reaction (PCR) and real-time PCR. Our highly controlled isolation method opens a new avenue for the study of subcellular functional mechanisms, enabling the identification of rare cells of potential functional or pathogenic consequence.

Phylodynamics for cell biologists.

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

Ancestry and evolution in cell biology

Phylodynamics Advances in experimental approaches for single-cell analysis allow in situ sequencing, genomic barcoding, and mapping of cell lineages within tissues and organisms. Large amounts of data have thus accumulated and present an analytical challenge. Stadler et al. recognized the need for conceptual and computational approaches to fully exploit these technological advances for the understanding of normal and disease states. The authors review ideas taken from phylodynamics of infectious disease and show how similar tree-building techniques can be applied to monitoring changes in somatic cell lineages for applications ranging from development and differentiation to cancer biology. Science , this issue p. [eaah6266][1] [1]: /lookup/doi/10.1126/science.aah6266

Droplet flow cytometry for single-cell analysis.

The interrogation of single cells has revolutionised biology and medicine by providing crucial unparalleled insights into cell-to-cell heterogeneity. Flow cytometry (including fluorescence-activated cell sorting) is one of the most versatile and high-throughput approaches for single-cell analysis by detecting multiple fluorescence parameters of individual cells in aqueous suspension as they flow past through a focus of excitation lasers. However, this approach relies on the expression of cell surface and intracellular biomarkers, which inevitably lacks spatial and temporal phenotypes and activities of cells, such as secreted proteins, extracellular metabolite production, and proliferation. Droplet microfluidics has recently emerged as a powerful tool for the encapsulation and manipulation of thousands to millions of individual cells within pico-litre microdroplets. Integrating flow cytometry with microdroplet architectures surrounded by aqueous solutions (e.g., water-in-oil-in-water (W/O/W) double emulsion and hydrogel droplets) opens avenues for new cellular assays linking cell phenotypes to genotypes at the single-cell level. In this review, we discuss the capabilities and applications of droplet flow cytometry (DFC). This unique technique uses standard commercially available flow cytometry instruments to characterise or select individual microdroplets containing single cells of interest. We explore current challenges associated with DFC and present our visions for future development.

Characterization and control of bacterial phenotypes at the single cell level

<p indent=0mm>Microbial biofilms comprise surface-associated, multicellular, highly structured matrix-enclosed, morphologically complex microbial communities. The subject of biofilms has received much attention, in part owing to scientific communities’ acknowledgements that biofilm formation of pathogen can cause many acute or chronic diseases that usually cannot be treated by antibiotic therapies, such as <italic>Pseudomonas aeruginosa</italic>, one of the highly prevalent opportunistic human pathogens, that is the leading cause of morbidity and mortality in immunocompromised patients and in patients suffering from cystic fibrosis (CF). We have learned that biofilm-forming bacteria are phenotypically distinct from those of planktonically grown cells, because they express genes in a pattern differs profoundly from that of their free-swimming, planktonic counterparts. Therefore, understanding the roles played by bacterial behaviors and phenotypes in the development of biofilms is an emerging link to disease pathogenesis. In the first half of this review, we present how to develop characteristic approaches by using high-throughput microscopical techniques together with automatic image-processing methods and customized microfluidic system to in situ visualize bacterial cells and investigate their behaviors at the single cell scale. We mainly focused on the pathogen of <italic>P. aeruginosa</italic> and provided an overview of recent related progress using those approaches in characterization of bacterial phenotypes at the single cell level in the process of biofilm formation, especially in areas of bacterial twitching motility, attachment phenotypes, social behaviors and architecture of biofilms. For instance, by monitoring single-cell motility behaviors, researchers observed single-cell twitching chemotaxis in developing biofilms and revealed that cells can modulate twitching motility behaviors to survive or thrive in slowly changing and locally heterogeneous natural environments. We then referred to optogenetics, which is a technology that allows targeted, fast control of precisely defined events in biological systems by expressing exogenous genes coding for light-sensitive proteins. In optogenetics, light as inducer can be applied more precisely in the concentration, time and space dimensions than traditional effectors such as chemical molecules. In the field of microbiology, different light responsive sensors, such as UV, blue, green, red and far-red transcriptional regulation systems, have been engineered, and application of these optogenetic systems enables little perturbations and unprecedented spatiotemporal resolution in controlling bacterial behaviors that can reveal new insights into biological function. For example, researchers have used blue light-switchable gene regulation system to optogenetic control of gene expression to spatiotemporally manipulate biofilm formation and pattern cells with high-resolution. These studies provide the ability to grow structured biofilms, with applications toward an improved understanding of natural biofilm communities, as well as the engineering of living biomaterials and bottom-up approaches to microbial consortia design. In addition, optogenetics enables characterization of bacterial gene circuit dynamics with optically programmed gene expression signals, which could advance understanding of gene circuit dynamics and cell signaling pathways. We also proposed that the development of optogenetics in bacteriology is limited by the requirement of engineered light-sensitive proteins for specific regulation networks and new methods for manipulation of living bacterial cells at the single-cell level. This review concludes with some expectations on the foreseeable future application of those single-cell analysis approaches in biofilms-related fields, and a discussion of innovative anti-biofilm strategies that are inspired from the studies of characterization and control of single-cell phenotypes.

Abstract B36: Identifying single-cell gene expression and EGFR mutation profile heterogeneity in NSCLC patients’ CTCs

Abstract The efficacy of targeted therapies was first shown in patients with non-small cell lung cancer (NSCLC) who possess activating epidermal growth factor receptor (EGFR) mutations that responded to tyrosine kinase inhibitors (TKIs). Current standard of care is to screen metastatic patients for the presence of EGFR mutations to determine if they qualify for TKI treatment. Due to the invasiveness of a tumor biopsy, this characterization is typically only done at the time of diagnosis. This single time-point determines the entire treatment plan. Despite strong initial response to TKI therapy, most patients develop resistance within months, most commonly by acquiring a secondary EGFR mutation. Identifying resistance through current monitoring techniques, such as measuring tumor volume, can lead to a delay in detection. Alternatively, circulating tumor cells (CTCs) are cancer cells present in the blood that provide access to tumor cells without the requirement of a biopsy. Shed from the tumor into the vasculature, CTCs circulate throughout the body before a fraction extravasate, leading to metastatic sites. CTCs have been shown to carry tumor-matched characteristics in both genotype and phenotype. Due to their ease of access, they can be used to serially track patients’ conditions to detect the early emergence of new tumor clones and, potentially, therapeutic resistance. One of the major limitations of CTCs’ clinical utility is their low abundance in the blood. Our group previously developed the Labyrinth, a high-throughput, label-free microfluidic technology, which enables rapid and efficient CTC enrichment. These CTCs are used for enumeration or single-cell transcriptomic analysis. Initial single-cell analysis was performed through highly multiplexed RT-qPCR that compared 96-gene expression profiles to unravel inter- and intrapatient CTC heterogeneity. We recently developed a complimentary single-cell analysis approach using digital PCR (dPCR) to detect the presence of sensitizing EGFR mutations, exon 19 deletion and L858R, and resistance mutation, T790M. dPCR is an ultrasensitive approach with single-molecule resolution, which enables detection and quantification at a single-cell level by partitioning the sample into individual PCR reaction droplets. This resolution can distinguish if a cell is homo- or heterozygous for a mutation and each allele’s relative expression level. We validated this system for single-cell analysis using lung cancer cell lines and demonstrated its agreement with qPCR results. We present initial findings of EGFR mutation screening in CTCs from a small cohort of NSCLC patients with known EGFR status. We highlight the single-cell heterogeneity of CTCs within a patient at both the gene expression and EGFR mutation level. By tracking these patients’ CTCs over time, we may be able to identify the emergence of resistant clones sooner than can be done in the clinic. This may allow for more rapid treatment modification and lead to improved patient outcomes. Citation Format: Sarah Owen, Ting-Wen Lo, Shamileh Fouladdel, Mina Zeinali, Evan Keller, Ebrahim Azizi, Nithya Ramnath, Sunitha Nagrath. Identifying single-cell gene expression and EGFR mutation profile heterogeneity in NSCLC patients’ CTCs [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr B36.

Comparison of spectral FRET microscopy approaches for single-cell analysis.

Förster resonance energy transfer (FRET) is a valuable tool for measuring molecular distances and the effects of biological processes such as cyclic nucleotide messenger signaling and protein localization. Most FRET techniques require two fluorescent proteins with overlapping excitation/emission spectral pairing to maximize detection sensitivity and FRET efficiency. FRET microscopy often utilizes differing peak intensities of the selected fluorophores measured through different optical filter sets to estimate the FRET index or efficiency. Microscopy platforms used to make these measurements include wide-field, laser scanning confocal, and fluorescence lifetime imaging. Each platform has associated advantages and disadvantages, such as speed, sensitivity, specificity, out-of-focus fluorescence, and Z-resolution. In this study, we report comparisons among multiple microscopy and spectral filtering platforms such as standard 2-filter FRET, emission-scanning hyperspectral imaging, and excitation-scanning hyperspectral imaging. Samples of human embryonic kidney (HEK293) cells were grown on laminin-coated 28 mm round gridded glass coverslips (10816, Ibidi, Fitchburg, Wisconsin) and transfected with adenovirus encoding a cAMP-sensing FRET probe composed of a FRET donor (Turquoise) and acceptor (Venus). Additionally, 3 FRET "controls" with fixed linker lengths between Turquoise and Venus proteins were used for inter-platform validation. Grid locations were logged, recorded with light micrographs, and used to ensure that whole-cell FRET was compared on a cell-by-cell basis among the different microscopy platforms. FRET efficiencies were also calculated and compared for each method. Preliminary results indicate that hyperspectral methods increase the signal-to-noise ratio compared to a standard 2-filter approach.