Tool For Single-cell Analysis Research Articles

Microfluidic flow sensor based on chronoamperometric measurements in a microchannel

Microfluidics explores the fluid behavior at micron scale and is therefore suited to applications that need precise manipulation of fluids at small length or volume scales. In a span of over two decades, microfluidics has become an essential tool for modern genome sequencing platforms, single-cell analysis and common diagnostic tests. Despite many successful applications, the field has various challenges: Fabrication processes are yet to transition to commercial materials, microfluidic systems are reliant on skilled operators, and supporting equipment. Technological innovations in automation are needed to enable easy to use, completely on-chip microfluidic systems. Sensors are a critical component of any automated system. Fluid and flow properties such as flow rate, pressure, temperature, viscosity are critical to applications in drug development, and material/chemical synthesis. The flow rate (μL/min or nL/min) predictions based on external (off-chip) flow measurements can lead to erroneous calculations. The present work explores a technique based on chronoamperometry to measure flow rates inside a microchannel. Chronoamperometry based redox cycling of aqueous NaCl was performed by using interdigitated electrodes (IDE). The sensor measures current transients (which are modulated by the flow rate of the solution) effected by redox reactions on surface of the electrodes. The sensor demonstrated a sensitivity of 0.016 (μL/min)−1 change in charge transfer per unit change in flow rate and 3σ resolution of 9.3 μL/min. The calibration curve is linear in the range 0 – 200 μL/min, with a limit of detection (LoD) of 5 μL/min, making it suitable for various microfluidics applications.

Fragment-Fusion Transformer: Deep Learning-Based Discretization Method for Continuous Single-Cell Raman Spectral Analysis.

Raman spectroscopy has become an important single-cell analysis tool for monitoring biochemical changes at the cellular level. However, Raman spectral data, typically presented as continuous data with high-dimensional characteristics, is distinct from discrete sequences, which limits the application of deep learning-based algorithms in data analysis due to the lack of discretization. Herein, a model called fragment-fusion transformer is proposed, which integrates the discrete fragmentation of continuous spectra based on their intrinsic characteristics with the extraction of intrafragment features and the fusion of interfragment features. The model integrates the intrinsic feature-based fragmentation of spectra with transformer, constructing the fragment transformer block for feature extraction within fragments. Interfragment information is combined through the pyramid design structure to improve the model's receptive field and fully exploit the spectral properties. During the pyramidal fusion process, the information gain of the final extracted features in the spectrum has been enhanced by a factor of 9.24 compared to the feature extraction stage within the fragment, and the information entropy has been enhanced by a factor of 13. The fragment-fusion transformer achieved a spectral recognition accuracy of 94.5%, which is 4% higher compared to the method without fragmentation and fusion processes on the test set of cell Raman spectroscopy identification experiments. In comparison to common spectral classification models such as KNN, SVM, logistic regression, and CNN, fragment-fusion transformer has achieved 4.4% higher accuracy than the best-performing CNN model. Fragment-fusion transformer method has the potential to serve as a general framework for discretization in the field of continuous spectral data analysis and as a research tool for analyzing the intrinsic information within spectra.

SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells

Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

A TEMPERATURE-CONTROLLED ON-CHIP HIGH-THROUGHPUT SINGLE-CELL CULTURE PLATFORM

Modern lab-on-a-chip and microfluidic technologies offer opportunities in developing portable cell culture platforms for growing and analyzing the behavior of living cells. We have proposed the magnetomicrofluidic chips, as a novel single-cell analysis tool, composed of microfluidic microchambers and microchannels overlaid on magnetic thin films designed to precisely transport magnetized single cells and magnetic particles with the help of hybrid hydrodynamic and magnetic forces. In order to provide the optimal temperature for the cells to grow on the chip, here we propose an on-chip microincubator at the single-cell level, without the need for a conventional incubator. An electronic circuit is used to read the chip temperature and control it with a feedback system. We perform finite element methods analysis to study the chip temperature profile, based on which the final chip is designed. We show that experimental results agree well with the simulation results. Using the proposed chip and the on-chip incubator, we transport thousands of single cells into the microchambers, culture them, and demonstrate their proper proliferation. The introduced chip can be used in crucial applications, such as drug screening, in which long-term study of the cells under a microscope, at the single-cell level, is required.

The effect of data transformation on low-dimensional integration of single-cell RNA-seq

BackgroundRecent developments in single-cell RNA sequencing have opened up a multitude of possibilities to study tissues at the level of cellular populations. However, the heterogeneity in single-cell sequencing data necessitates appropriate procedures to adjust for technological limitations and various sources of noise when integrating datasets from different studies. While many analysis procedures employ various preprocessing steps, they often overlook the importance of selecting and optimizing the employed data transformation methods.ResultsThis work investigates data transformation approaches used in single-cell clustering analysis tools and their effects on batch integration analysis. In particular, we compare 16 transformations and their impact on the low-dimensional representations, aiming to reduce the batch effect and integrate multiple single-cell sequencing data. Our results show that data transformations strongly influence the results of single-cell clustering on low-dimensional data space, such as those generated by UMAP or PCA. Moreover, these changes in low-dimensional space significantly affect trajectory analysis using multiple datasets, as well. However, the performance of the data transformations greatly varies across datasets, and the optimal method was different for each dataset. Additionally, we explored how data transformation impacts the analysis of deep feature encodings using deep neural network-based models, including autoencoder-based models and proto-typical networks. Data transformation also strongly affects the outcome of deep neural network models.ConclusionsOur findings suggest that the batch effect and noise in integrative analysis are highly influenced by data transformation. Low-dimensional features can integrate different batches well when proper data transformation is applied. Furthermore, we found that the batch mixing score on low-dimensional space can guide the selection of the optimal data transformation. In conclusion, data preprocessing is one of the most crucial analysis steps and needs to be cautiously considered in the integrative analysis of multiple scRNA-seq datasets.

High-precision screening and sorting of double emulsion droplets.

Mounting evidence suggests that cell populations are extremely heterogeneous, with individual cells fulfilling different roles within the population. Flow cytometry (FC) is a high-throughput tool for single-cell analysis that works at high optical resolution. Sub-populations with unique properties can be screened, isolated and sorted through fluorescence-activated cell sorting (FACS), using intracellular fluorescent products or surface-tagged fluorescent products of interest. However, traditional FC and FACS methods cannot identify or isolate cells that secrete extracellular products of interest. Double emulsion (DE) droplets are an innovative approach to retaining these extracellular products so cells producing them can be identified and isolated with FC and FACS. The water-in-oil-in-water structure makes DE droplets compatible with the sheath flow of flow cytometry. Single cells can be encapsulated with other reagents into DEs, which act as pico-reactors. These droplets allow biological activities to take place while allowing for cell cultivation monitoring, rare mutant identification, and cellular events characterization. However, using DEs in FACS presents technical challenges, including rupture of DEs, poor accuracy and low sorting efficiency. This study presents high-performance sorting using fluorescent beads (as simulants for cells). This study aims to guide researchers in the use of DE-based flow cytometry, offering insights into how to resolve the technical difficulties associated with DE-based screening and sorting using FC.

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators

Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells.

SingmiR: a single-cell miRNA alignment and analysis tool.

Single-cell RNA sequencing(RNA-seq) has revolutionized our understanding of cell biology, developmental and pathophysiological molecular processes, paving the way toward novel diagnostic and therapeutic approaches. However, most of the gene regulatory processes on the single-cell level are still unknown, including post-transcriptional control conferred by microRNAs (miRNAs). Like the established single-cell gene expression analysis, advanced computational expertise is required to comprehensively process newly emerging single-cell miRNA-seq datasets. A web server providing a workflow tailored for single-cell miRNA-seq data with a self-explanatory interface is currently not available. Here, we present SingmiR, enabling the rapid (pre-)processing and quantification of human miRNAs from noncoding single-cell samples. It performs read trimming for different library preparation protocols, generates automated quality control reports and provides feature-normalized count files. Numerous standard and advanced analyses such as dimension reduction, clustered feature heatmaps, sample correlation heatmaps and differential expression statistics are implemented. We aim to speed up the prototyping pipeline for biologists developing single-cell miRNA-seq protocols on small to medium-sized datasets. SingmiR is freely available to all users without the need for a login athttps://www.ccb.uni-saarland.de/singmir.

Bento: a toolkit for subcellular analysis of spatial transcriptomics data

The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell–cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene–gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

Abstract 7430: Development and demonstration of a user-friendly application to explore single-cell atlas for prostate cancer (PCa) and analyze newly generated single-cell and spatial PCa datasets

Abstract Background: Single-cell atlases provide a high-resolution and tissue-specific depiction of cellular state at the transcriptomic level. Newer technologies like spatial transcriptomics, MALDI, and long-read sequencing introduce orthogonal modalities to single-cell data, making the analyses of large atlases even more complex. RShiny package in R is used to develop user-friendly GUI applications that could enable users to use analysis tools in an interactive manner, thus minimizing the requirement of prior programming experience. Methods: Publicly available scRNA-seq datasets were collected for a wide range of prostate-related pathologies. The datasets were evaluated for their quality based on cell count and availability of the appropriate markers in each compartment. Finally, 11 datasets out of 16 datasets were selected for building integrated datasets. These datasets were filtered for cell types with &amp;lt;10% mitochondrial RNA, and cells with &amp;lt;500 genes were removed from the datasets. The datasets were then clustered to evaluate the effects of cell cycle, dissociation-induced stress, and the presence of doublets. The quality-controlled datasets were batch-corrected using the Harmony package and the harmonized principal components were used in Seurat for clustering the integrated data. Tools used for the analysis of single-cell datasets, beginning from raw counts, have been developed in R with a focus on modularity. These tools are packaged into a simple and intuitive RShiny application which follows the workflow shown in Figure 1a. Results: The atlas obtained include samples from healthy prostate, benign prostate hyperplasia, primary prostate tumor, ICC/IDC, castration-resistant prostate cancer (CRPC), and Neuroendocrine prostate cancer (NEPC). The dataset includes 72 patients and ~320k cells with a median number of ~1600 genes expressed. We were able to distinctly identify Luminal, Hillock, Club, Basal, Neuroendocrine, Endothelial, Fibroblast, Smooth Muscle, Myeloid, T, B, NK, Mast, and Plasma cells in the integrated along with some unknown cellular subtypes in the epithelial compartment. The integrated data was manually annotated using cell type-specific markers. The single-cell atlas obtained was weaved with the workflow tools into an RShiny app. Conclusions: Besides being a useful reference for PCa, the atlas would find extensive use in understanding the transcriptomic landscape of PCa biology. The integrated dataset will also prove crucial in developing bulk RNA sequencing deconvolution and single-cell imputation algorithms. Finally, we aim to provide a user-friendly interface to the prostate cancer community for using the single-cell PCa atlas and analysis tools with minimal computational requirements. Citation Format: Shivang Sharma, Eugene Shenderov. Development and demonstration of a user-friendly application to explore single-cell atlas for prostate cancer (PCa) and analyze newly generated single-cell and spatial PCa datasets [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 7430.

Abstract 4956: A fast and efficient bioinformatics analysis workflow for processing reads from single-cell multiomics assays captured on a microwell-based platform

Abstract Here, we present a significant update: version 2.0 of our sequencing analysis, it is a comprehensive primary analysis workflow that is up to 7X faster and consumes 2X less disk space than the previous release. We used it to process a library with 20,000 human PBMCs sequenced to a depth of 1.2 billion reads in 2.5 hours of wall-clock time. The pipeline can be used with reads from single-cell whole transcriptome, targeted mRNA, surface antigens (AbSeq), TCR/BCR and Sample Tag libraries captured on the BD Rhapsody™ System platform. The major processing steps include Quality Filtering, Cell Barcode Identification, Read Alignment, Feature Assignment, UMI Error Correction, Identification of Putative Cells, Sample De-Multiplexing, Dimensionality Reduction for Visualization and VDJ Contig Assembly and Annotation. Output files and metrics are available in easy-to-digest formats, including an html report with dynamic visualization. Integrated outputs in Seurat and Scanpy formats combine expression matrices and all cell annotation metadata (e.g., predicted cell type, sample assignment, TCR/BCR sequence and gene segments). These pre-generated files are ready to load into popular single-cell analysis tools. The pipeline uses CWL as the workflow manager and makes use of custom code written in C++ and Python along with various open-source packages for data processing. The pipeline can also process reads from non-human species and includes built-in support for specifying and building custom reference genome indices. In support of high-throughput multiomic discovery studies enabled by the BD Rhapsody™ HT Xpress System, this pipeline has been tested with datasets containing more than 800,000 putative cells and 14.5 billion reads. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD, the BD Logo and BD Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2023 BD. All rights reserved. NPM-2535 (v1.0) 1023 Citation Format: Raghavendra Padmanabhan, Brent Weichel, Amie Radenbaugh, Yuefu Jiang, Charles Weeks, Thomas McCarthy, Youngsook Kim, Anthony Berno, Devon Jensen. A fast and efficient bioinformatics analysis workflow for processing reads from single-cell multiomics assays captured on a microwell-based platform [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 4956.

Microfluidic device featuring micro-constrained channels for multi-parametric assessment of cellular biomechanics and high-precision mechanical phenotyping of gastric cells

BackgroundCellular biomechanics plays a significant role in the regulation of cellular physiological and pathological processes. In recent years, multiple methods have been developed to evaluate cellular biomechanics, such as atomic force microscopy (AFM), micropipette aspiration, and magnetic tweezers. However, most of these methods only focus on a single parameter and cannot automate the process at a high-efficiency level. A novel microfluidic method is necessary to achieve the simultaneous multi-parametric measurement of cellular biomechanics and high-precision cellular mechanical phenotyping at high throughput. ResultsTo tackle the issue concerning the low-throughput and cellular single-parameter evaluation, we designed and fabricated a microfluidic chip featuring multiple micro-constrained channels structure, providing a simultaneous multi-parametric assessment of cellular biomechanics, including elastic modulus, recovery capability, and deformability. We compared the biomechanical properties of normal human gastric mucosal epithelial cells (GES-1) and human gastric cancer cells (AGS and MKN-45) by the chip. Results demonstrated that the elastic modulus of GES-1, AGS, and MKN-45 cells decreased sequentially, which was the opposite of their invasiveness and metastasis potential, suggesting the inverse correlation between cellular elastic modulus and malignancy. Meanwhile, the recovery capability and deformability of GES-1, AGS, and MKN-45 cells increased sequentially, demonstrating the positive correlation between cellular deformability and malignancy. Furthermore, multiple parameters were used to distinguish gastric cancer cells from normal gastric cells via machine learning. An accuracy of over 94.8% for identifying gastric cancer cells was achieved. SignificanceThis study provides a deep insight into the biophysical mechanism of gastric cancer metastasis at the single-cell level and possesses great potential to function as a valuable tool for single-cell analysis, thereby facilitating high-precision and high-throughput discrimination of cellular phenotypes that are not easily discernible through single-marker analysis.

Microfluidic device for the high-throughput and selective encapsulation of single target cells.

Droplet-based microfluidic technologies for encapsulating single cells have rapidly evolved into powerful tools for single-cell analysis. In conventional passive single-cell encapsulation techniques, because cells arrive randomly at the droplet generation section, to encapsulate only a single cell with high precision, the average number of cells per droplet has to be decreased by reducing the average frequency at which cells arrive relative to the droplet generation rate. Therefore, the encapsulation efficiency for a given droplet generation rate is very low. Additionally, cell sorting operations are required prior to the encapsulation of target cells for specific cell type analysis. To address these challenges, we developed a cell encapsulation technology with a cell sorting function using a microfluidic chip. The microfluidic chip is equipped with an optical detection section to detect the optical information of cells and a sorting section to encapsulate cells into droplets by controlling a piezo element, enabling active encapsulation of only the single target cells. For a particle population including both targeted and non-targeted particles arriving at an average frequency of up to 6000 particles per s, with an average number of particles per droplet of 0.45, our device maintained a high purity above 97.9% for the single-target-particle droplets and achieved an outstanding throughput, encapsulating up to 2900 single target particles per s. The proposed encapsulation technology surpasses the encapsulation efficiency of conventional techniques, provides high efficiency and flexibility for single-cell research, and shows excellent potential for various applications in single-cell analysis.

Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology.

Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.

MtFociCounter for automated single-cell mitochondrial nucleoid quantification and reproducible foci analysis.

Mitochondrial DNA (mtDNA) encodes the core subunits for OXPHOS, essential in near-all eukaryotes. Packed into distinct foci (nucleoids) inside mitochondria, the number of mtDNA copies differs between cell-types and is affected in several human diseases. Currently, common protocols estimate per-cell mtDNA-molecule numbers by sequencing or qPCR from bulk samples. However, this does not allow insight into cell-to-cell heterogeneity and can mask phenotypical sub-populations. Here, we present mtFociCounter, a single-cell image analysis tool for reproducible quantification of nucleoids and other foci. mtFociCounter is a light-weight, open-source freeware and overcomes current limitations to reproducible single-cell analysis of mitochondrial foci. We demonstrate its use by analysing 2165 single fibroblasts, and observe a large cell-to-cell heterogeneity in nucleoid numbers. In addition, mtFociCounter quantifies mitochondrial content and our results show good correlation (R=0.90) between nucleoid number and mitochondrial area, and we find nucleoid density is less variable than nucleoid numbers in wild-type cells. Finally, we demonstrate mtFociCounter readily detects differences in foci-numbers upon sample treatment, and applies to Mitochondrial RNA Granules and superresolution microscopy. mtFociCounter provides a versatile solution to reproducibly quantify cellular foci in single cells and our results highlight the importance of accounting for cell-to-cell variance and mitochondrial context in mitochondrial foci analysis.

Smart Droplet Microfluidic System for Single-Cell Selective Lysis and Real-Time Sorting Based on Microinjection and Image Recognition.

Single-cell analysis has important implications for understanding the specificity of cells. To analyze the specificity of rare cells in complex blood and biopsy samples, selective lysis of target single cells is pivotal but difficult. Microfluidics, particularly droplet microfluidics, has emerged as a promising tool for single-cell analysis. In this paper, we present a smart droplet microfluidic system that allows for single-cell selective lysis and real-time sorting, aided by the techniques of microinjection and image recognition. A custom program evolved from Python is proposed for recognizing target droplets and single cells, which also coordinates the operation of various parts in a whole microfluidic system. We have systematically investigated the effects of voltage and injection pressure applied to the oil-water interface on droplet microinjection. An efficient and selective droplet injection scheme with image feedback has been demonstrated, with an efficiency increased dramatically from 2.5% to about 100%. Furthermore, we have proven that the cell lysis solution can be selectively injected into target single-cell droplets. Then these droplets are shifted into the sorting area, with an efficiency for single K562 cells reaching up to 73%. The system function is finally explored by introducing complex cell samples, namely, K562 cells and HUVECs, with a success rate of 75.2% in treating K562 cells as targets. This system enables automated single-cell selective lysis without the need for manual handling and sheds new light on the cooperation with other detection techniques for a broad range of single-cell analysis.

Abstract A066: Molecular phenotyping in single cell RNA sequencing allows for identification of common populations across studies and platforms

Abstract Single cell RNA sequencing (scRNA seq) has become a prominent tool towards understanding models of prostate cancer. However, there are limited approaches geared towards collating these experiments into a unified framework for understanding discoveries across studies. In our lab, we have performed scRNA seq on multiple organoid models, across a spectrum of phenotypes including amphicrine, adenocarcinoma, and neuroendocrine disease, as well as performed lineage barcode tracing in a subset of these experiments as a means of identifying lineage transitions. Additionally, we have scRNA seq on a robust patient-derived xenograft (PDX) model of metastasis, consisting of experiments from primary and metastatic PDX models collected from multiple tissues including both intact and castrated backgrounds. Along with data generated by our lab, we accessed published scRNA seq studies from other labs which broadened the pool of experiments to include data from cell lines and multiple single cell analysis platforms. In order to perform computational molecular phenotyping of the scRNA seq experiments, we curated gene sets from the literature and applied principal components analysis (PCA) to them as our molecular phenotyping scoring approach which is widely used by previous studies. Because of the diverse background of the studies being incorporated into our molecular phenotyping, we used the bioconductor stack of single cell analysis tools to perform quality control and removal of batch effects. We observe that molecular phenotyping of samples across different backgrounds shows common populations typically associated with proliferation and stemness, and that unique populations present in each sample are usually more associated with terminal differentiation and senescence. In addition to identifying common and unique populations across samples and platforms, we also can assess individual genes on their importance in the molecular phenotyping approach. The use of PCA to generate our molecular phenotyping score allows the rotation matrix to be used as an additional metric when ranking genes for biological importance in concert with commonly used metrics such as variability and deviance. When combining the global metrics with the rotation matrix for each gene set, genes can be arranged in a network to determine key genes for molecular phenotyping, allowing for more concise gene sets that decrease the computational overhead for performing the molecular phenotyping. Overall, our work suggests that a unified framework of analysis can yield new insights into scRNA seq studies by identifying common populations present across sample backgrounds and different scRNA seq technologies. Additionally, genes can be arranged into networks based on biological significance derived from global statistical analysis as well as molecular phenotyping to identify more concise gene signatures, decreasing computational overhead and highlighting genes with excessive contribution to biological state. Citation Format: Brian Capaldo, Michael Beshiri, Juan Yin, Kathy Kelly. Molecular phenotyping in single cell RNA sequencing allows for identification of common populations across studies and platforms [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A066.

Probing Protein 4'-Phosphopantetheinylation in Single Living Cells.

4'-Phosphopantetheinylation (4PPTylation) of proteins, which is derived from the hydrolysis of coenzyme A (CoA), is an essential post-translational modification participating in biosynthetic and metabolic pathways. However, due to the lack of specific recognition ligands as well as the shortage of sensitive analytical tools for single-cell analysis, the in-depth exploration of new cellular functions and mechanisms of protein 4PPTylation has been much hampered. In this study, we rationally engineered CoA-imprinted Raman nanotags for the specific recognition of 4PPTylation and thereby developed a molecularly imprinted polymer (MIP)-based plasmonic immunosandwich assay (PISA) for facile probing the 4PPTylation of ALDH1L1 in single cells. The molecularly imprinted nanotags exhibited excellent binding properties, giving a dissociation constant of 10-6 M and cross-reactivity values of less than 10%. The MIP-based PISA enabled the specific and sensitive detection of the level of 4PPTylated ALDH1L1 in single living cells. Particularly, monitoring of the fluctuation of 4PPTylated ALDH1L1 in single cells under simulation by an inhibitor (methotrexate) that acts on a different metabolism pathway was achieved, implying possible crosstalk between two different pathways in folate metabolism. Thus, the imprinted Raman nanotags-PISA provides a promising analytical tool with a single-cell resolution for exploring new functions and elucidating their mechanisms of protein 4PPTylation.

Abstract 2091: Open-source single-cell spatial image analysis pipeline for ten-plex immunofluorescence of histological prostate cancer tissue sections

Abstract Introduction: Multiplexed fluorescence immunohistochemistry (mfIHC) allows the detection of multiple protein markers on the same histological tissue section, generating valuable information on specific cell subsets and cellular states in their spatial tissue context. Here, we demonstrate a proof-of-concept study for profiling different tumor microenvironment cell subsets in formalin-fixed paraffin-embedded prostate cancer tissue microarrays. Materials and methods: We applied automated mfIHC simultaneous staining of 31 slides with 3.5-µm sections using an Autostainer (Epredia, Kalamazoo, USA). The protocol included four rounds of cyclic staining and scanning of the slides with AxioScanZ.1 (Zeiss, Germany). Each cycle included DAPI for nuclear staining. Here, we applied this pipeline to detect the following targets: D2-40 (lymphatics), CD3 (T lymphocytes), CD56 (NK cells/neural cells/neuroendocrine cells), CD11b+ myeloid cells, PDGFRB (fibroblasts/pericytes), CD20 (B lymphocytes), CD31 (endothelium), CD45 (leukocytes), HLADR+ antigene-presenting cells, as well as a cocktail with PanCK and ECADH to detect epithelial cells. Three different prostate cancer TMA cohorts with n=907 patient samples were analyzed here using this approach.The scanned images were exported as Big TIFF individual channel whole-slide images. Individual TMA spot images were marked with Fiji ROI1 1-Click Tool macro (ImageJ 1.53t), and separate rounds were registered with Python (version 3.11.0, Python Software Foundation) using DAPI channel as reference. Nuclear segmentation was created with nucleAIzer trained modified Mask R-CNN model (https://github.com/lopaavol/nuclei_segmentation). All TMA spot images were run through image curation, where artefacts are excluded by machine learning using ilastik software (version 1.3.3post3). Then, CellProfiler (version 4.2.1) was applied to combine image curated tissue masks and segmented nuclei for cell-based channel intensity measurements. Finally, Jupyter Notebook was applied to classify cell subsets into specific phenotypes using intensity-based thresholding for each channel. Results and Conclusions: We present a detailed pipeline for 10-plex mfIHC with open-source single-cell image analysis tools. The approach can be applied to histological laboratories when combined with a standard five fluorescent channel scanner or imager and with basic computing skills. As the system applies secondary antibodies for the detection, it is flexible for optimizing new antibody panels, and the user is not limited to pre-labelled antibody panels often connected with commercial staining and imaging systems. The approach is especially suited for large-scale high-throughput tissue profiling studies when high n-values are needed for linking associated patient clinical data with spatial phenotype data. Citation Format: Jenni Säilä, Annabrita Schoonenberg, Katja Välimäki, Timo-Pekka Lehto, Carolin Stürenberg, Andrew Erickson, Antti Rannikko, Olli Kallioniemi, Lassi Paavolainen, Tuomas Mirtti, Teijo Pellinen. Open-source single-cell spatial image analysis pipeline for ten-plex immunofluorescence of histological prostate cancer tissue sections [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2091.