Single-cell Phenotyping Research Articles

Differential gliding motility responses of Chryseobacterium sp. strain PMSZPI isolated from uranium ore deposit on hard and soft substrates

The Bacteroidota bacterium, Chryseobacterium sp. strain PMSZPI isolated from sub-surface soil of uranium ore deposit was shown to move on solid surfaces via gliding motility resulting in the formation of thin spreading colonies. In this study, we attempted to understand the influence of the surfaces, soft or hard/rigid, on the motility behaviour of PMSZPI cells. The computational tool T9GPred in combination with LC-MS/MS analysis established the presence of orthologs of vital gliding motility proteins in PMSZPI. We analyzed the single cell or population motility phenotypes of PMSZPI under spreading and non-spreading conditions. A low percentage of agar or soft agar (0.35%) with low nutrient levels induced more active gliding motility in individual cells leading to increased colony spreading. Microscopic analyses indicated the self-assembly of the gliding cells into irregular edged or spherical microcolonies based on the agar concentration. Cells moved at a speed of 0.2 to 0.6 µm s-1 on low-percentage gliding permissive agar (0.35%) surface in contrast to significant inhibition of motility on rigid or hard agar (1.5%) surface. RNA sequencing and real-time quantitative PCR (qPCR) analysis revealed increased expression of gliding motility genes under low agar conditions consistent with increased spreading behaviour. These findings provide the first glimpse into the gliding motility behaviour of a Bacteroidota bacterium from metal enriched environment that apparently could have implications on bacterial adaptation to changing surface environments.

Long-Term Stable and Multifeature Microfluidic Impedance Flow Cytometry Based on a Constricted Channel for Single-Cell Mechanical Phenotyping.

The microfluidic impedance flow cytometer (m-IFC) using constricted microchannels is an appealing choice for the high-throughput measurement of single-cell mechanical properties. However, channels smaller than the cells are susceptible to irreversible blockage, extremely affecting the stability of the system and the throughput. Meanwhile, the common practice of extracting a single quantitative index, i.e., total cell passage time, through the constricted part is inadequate to decipher the complex mechanical properties of individual cells. Herein, this study presents a long-term stable and multifeature m-IFC based on a constricted channel for single-cell mechanical phenotyping. The blockage problem is effectively overcome by adding tiny xanthan gum (XG) polymers. The cells can pass through the constricted channel at a flow rate of 500 μL/h without clogging, exhibiting high throughput (∼240 samples per second) and long-term stability (∼2 h). Moreover, six detection regions were implemented to capture the multiple features related to the whole process of a single cell passing through the long-constricted channel, e.g., creep, friction, and relaxation stages. To verify the performance of the multifeature m-IFC, cells treated with perturbations of microtubules and microfilaments within the cytoskeleton were detected, respectively. It suggests that the extracted features provide more comprehensive clues for single-cell analysis in structural and mechanical transformation. Overall, our proposed multifeature m-IFC exhibits the advantages of nonclogging and high throughput, which can be extended to other cell types for nondestructive and real-time mechanical phenotyping in cost-effective applications.

Droplets can enhance microcapsule deformation in channel flow

The dynamics of soft microparticles enclosed in a droplet flowing in a channel is an unexplored fundamental problem that lies at the heart of numerous applications, including droplet-based microfluidics, tissue engineering and smart material synthesis. Here we show that enclosing a flexible capsule into a droplet can amplify the capsule’s deformation parameters in channel flow by up to two orders of magnitude. Previously unreported capsule equilibrium shapes in channel flow, including an oblate spheroid and a reversed bullet, have also been discovered. We propose two theoretical models to predict the equilibrium position of the capsule inside the droplet, and estimate the capsule deformation, respectively. The present study provides an effective but simple approach to enhance and control the deformation of soft particles in a flowing suspension, which may inspire widespread applications, from high-throughput single-cell mechanical phenotyping, enhanced cross-membrane drug delivery, to manufacturing shape-controlled non-spherical particles and artificial cells.

Automated High-Throughput Atomic Force Microscopy Single-Cell Nanomechanical Assay Enabled by Deep Learning-Based Optical Image Recognition.

Mechanical forces are essential for life activities, and the mechanical phenotypes of single cells are increasingly gaining attention. Atomic force microscopy (AFM) has been a standard method for single-cell nanomechanical assays, but its efficiency is limited due to its reliance on manual operation. Here, we present a study of deep learning image recognition-assisted AFM that enables automated high-throughput single-cell nanomechanical measurements. On the basis of the label-free identification of the cell structures and the AFM probe in optical bright-field images as well as the consequent automated movement of the sample stage and AFM probe, the AFM probe tip could be accurately and sequentially moved onto the specific parts of individual living cells to perform a single-cell indentation assay or single-cell force spectroscopy in a time-efficient manner. The study illustrates a promising method based on deep learning for achieving operator-independent high-throughput AFM single-cell nanomechanics, which will benefit the application of AFM in mechanobiology.

Classifying cancer-associated fibroblasts—The good, the bad, and the target

Cancer-associated fibroblasts (CAFs) are heterogeneous and ubiquitous stromal cells within the tumor microenvironment (TME). Numerous CAF types have been described, typically using single-cell technologies such as single-cell RNA sequencing. There is no general classification system for CAFs, hampering their study and therapeutic targeting. We propose a simple CAF classification system based on single-cell phenotypes and spatial locations of CAFs in multiple cancer types, assess how our scheme fits within current knowledge, and invite the CAF research community to further refine it.

Mantis: High-throughput 4D imaging and analysis of the molecular and physical architecture of cells.

High-throughput dynamic imaging of cells and organelles is essential for understanding complex cellular responses. We report Mantis, a high-throughput 4D microscope that integrates two complementary, gentle, live-cell imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. Additionally, we report shrimPy (Smart High-throughput Robust Imaging and Measurement in Python), an open-source software for high-throughput imaging, deconvolution, and single-cell phenotyping of 4D data. Using Mantis and shrimPy, we achieved high-content correlative imaging of molecular dynamics and the physical architecture of 20 cell lines every 15 min over 7.5 h. This platform also facilitated detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to perturbations, and live-cell optical screens to dissect gene regulatory networks.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis.

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells.

High-throughput dynamic imaging of cells and organelles is essential for understanding complex cellular responses. We report Mantis, a high-throughput 4D microscope that integrates two complementary, gentle, live-cell imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. Additionally, we report shrimPy, an open-source software for high-throughput imaging, deconvolution, and single-cell phenotyping of 4D data. Using Mantis and shrimPy, we achieved high-content correlative imaging of molecular dynamics and the physical architecture of 20 cell lines every 15 minutes over 7.5 hours. This platform also facilitated detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to perturbations, and live-cell optical screens to dissect gene regulatory networks.

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

Massively parallel in vivo Perturb-seq reveals cell-type-specific transcriptional networks in cortical development

Leveraging AAVs' versatile tropism and labeling capacity, we expanded the scale of invivo CRISPR screening with single-cell transcriptomic phenotyping across embryonic to adult brains and peripheral nervous systems. Through extensive tests of 86 vectors across AAV serotypes combined with a transposon system, we substantially amplified labeling efficacy and accelerated invivo gene delivery from weeks to days. Our proof-of-principle in utero screen identified the pleiotropic effects of Foxg1, highlighting its tight regulation of distinct networks essential for cell fate specification of Layer 6 corticothalamic neurons. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% by lentivirus, to achieve analysis of over 30,000 cells in one experiment and enable massively parallel invivo Perturb-seq. Compatible with various phenotypic measurements (single-cell or spatial multi-omics), it presents a flexible approach to interrogate gene function across cell types invivo, translating gene variants to their causal function.

High-throughput mechanical phenotyping and transcriptomics of single cells

The molecular system regulating cellular mechanical properties remains unexplored at single-cell resolution mainly due to a limited ability to combine mechanophenotyping with unbiased transcriptional screening. Here, we describe an electroporation-based lipid-bilayer assay for cell surface tension and transcriptomics (ELASTomics), a method in which oligonucleotide-labelled macromolecules are imported into cells via nanopore electroporation to assess the mechanical state of the cell surface and are enumerated by sequencing. ELASTomics can be readily integrated with existing single-cell sequencing approaches and enables the joint study of cell surface mechanics and underlying transcriptional regulation at an unprecedented resolution. We validate ELASTomics via analysis of cancer cell lines from various malignancies and show that the method can accurately identify cell types and assess cell surface tension. ELASTomics enables exploration of the relationships between cell surface tension, surface proteins, and transcripts along cell lineages differentiating from the haematopoietic progenitor cells of mice. We study the surface mechanics of cellular senescence and demonstrate that RRAD regulates cell surface tension in senescent TIG-1 cells. ELASTomics provides a unique opportunity to profile the mechanical and molecular phenotypes of single cells and can dissect the interplay among these in a range of biological contexts.

Single-cell ionic current phenotyping elucidates non-canonical features and predictive potential of cardiomyocytes during automated drug experiments.

All new drugs must go through preclinical screening tests to determine their proarrhythmic potential. While these assays effectively filter out dangerous drugs, they are too conservative, often misclassifying safe compounds as proarrhythmic. In this study, we attempt to address this shortcoming with a novel, medium-throughput drug-screening approach: we use an automated patch-clamp system to acquire optimized voltage clamp (VC) and action potential (AP) data from human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) at several drug concentrations (baseline, 3×, 10× and 20× the effective free plasma concentrations). With our novel method, we show correlations between INa block and upstroke slowing after treatment with flecainide or quinine. Additionally, after quinine treatment, we identify significant reductions in current during voltage steps designed to isolate If and IKs. However, we do not detect any IKr block by either drug, and upon further investigation, do not see any IKr present in the iPSC-CMs when prepared for automated patch experiments (i.e. in suspension) - this is in contrast to similar experiments we have conducted with these cells using the manual patch setup. In this study, we: (1) present a proof-of-concept demonstration of a single-cell medium-throughput drug study, and (2) characterize the non-canonical electrophysiology of iPSC-CMs when prepared for experiments in a medium-throughput setting. KEY POINTS: Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer potential as an in vitro model to study the proarrhythmic potential of drugs, but insights from these cells are often limited by the low throughput of manual patch-clamp. In this study, we use a medium-throughput automated patch-clamp system to acquire action potential (AP) and complex voltage clamp (VC) data from single iPSC-CMs at multiple drug concentrations. A correlation between AP upstroke and INa transients was identified and drug-induced changes in ionic currents found. We also characterize the substantially altered physiology of iPSC-CMs when patched in an automated system, suggesting the need to investigate differences between manual and automated patch experiments.

Abstract PO5-27-02: A Tumor Emboli Platform for Morphometric Analysis, Drug screening and for Intravital Optical Imaging in a Murine Model to Simulate Clinicopathological Features of Inflammatory Breast Cancer

Abstract Background: Inflammatory breast cancer (IBC) is distinguished by the presence of tumor emboli, clusters of tumor cells, found within breast parenchyma and dermal lymphatic vessels. These emboli serve as a crucial clinicopathological hallmark, believed to underlie the characteristic diffuse growth pattern and aggressive progression observed in IBC patients, with nearly 30% of patients presenting with metastatic disease at diagnosis. However, the rarity of this cancer and the absence of a solid tumor mass pose challenges in obtaining biospecimens, particularly those with evidence of tumor emboli. To address this pressing need for preclinical models that replicate this distinctive tumor growth pattern, we describe a tumor emboli culture platform that facilitates live imaging, enables compound screening by assessing various phenotypes of the emboli, and permits evaluation of emboli in an in vivo murine model. Methods: The tumor emboli culture system was evaluated for physiological measurements corresponding to dermal lymphatic vessels and subsequently employed to generate tumor emboli from diverse patient-derived cell lines and PDX. Confocal microscopy was employed to characterize cellular information across multiple slices of the tumor emboli. This provided a comprehensive, multi-parametric analysis of single-cell and spheroid phenotypes, including measurements of number, size, shape, and viability of the organoids. Furthermore, we developed imaging algorithms to evaluate phenotypic changes in tumor emboli in response to anti-cancer drug treatment. We also optimized a technique that allows for in vivo tumor emboli implantation including in a transgenic mice with red fluorescent lymphatic vasculature that were surgically implanted with a dorsal skin window chamber. This allowed for intravital optical imaging of the local tumor microenvironment and adjacent skin. Results: Our culture platform enables live imaging of the tumor emboli generated over a span of 7-10 days, maintaining key parameters including kinematic and dynamic viscosity, density along with sheer wall stress within the reported physiological range for lymphatics. Through higher resolution confocal image acquisition and multi-parametric analysis, we conducted particle counting and classification, providing statistically significant insights into the integrity of tumor emboli over time. Additionally, the developed algorithms allowed for quantitative analysis of images from tumor emboli cultures subjected to anti-cancer drugs, revealing disparities in drug efficacy on tumor emboli integrity and dispersion including inter-cluster space within the emboli. Intravital optical imaging, coupled with quantitative analysis of dispersion characteristics of the tumor emboli implanted in the murine model over 5-6 days, demonstrated a diffuse spread reminiscent of observations in IBC patients. Furthermore, immunohistochemistry staining showed marked Ki67, CD45, F4-80 revealing a proliferative tumor microenvironment with high levels of macrophage infiltration. Conclusions: As of now specific targeted therapies for IBC patients remain elusive. Our culture platform for tumor emboli, when combined with advanced imaging techniques both in vitro and in vivo and traditional approaches like immunohistochemistry and genomic evaluation, forms a powerful toolkit for assessing the phenotype and dispersion characteristics of this unique pathological feature in IBC. This preclinical model offers crucial insights, potentially paving the way for precision medicine strategies in the treatment of this understudied cancer. Funding in part from American Cancer Society Mission Boost MBG-20-141-01-MBG grant (GRD), Department of Defense W81XWH-20-1-0153 (GRD), Duke Consortium for Inflammatory Breast Cancer Education (AB, CW, RE). Citation Format: Dorababu Sannareddy, Theresa Charity, Alexandra Bennion, Caroline Way, Edwin Yu, Ralph Erdmann, Gregory M Palmer, Gayathri R Devi. A Tumor Emboli Platform for Morphometric Analysis, Drug screening and for Intravital Optical Imaging in a Murine Model to Simulate Clinicopathological Features of Inflammatory Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-27-02.

Label-free morphological profiling and isolation of immune cell subsets using VisionSort, a novel, AI-based flow cytometry platform.

Abstract Identification, characterization, and minimally invasive isolation of specific populations of human immune cells are critical for understanding and treating disease. Here we present data on label-free identification of three immune cell subsets by morphological profiling using VisionSort; a label-free, artificial intelligence (AI)-driven cellular analysis and sorting platform. By capturing single-cell digital phenotypes, we characterized mouse T-cells and generated ‘ground truth’ functional profiles for activated and non-activated T cells. A set of machine-learning derived classifiers was generated to identify these phenotypic classes in unlabeled T-cell subsets. The classifier showed an area under the curve (AUC) performance for differentiating between phenotypically defined T cell populations of 0.917. In addition, by using unsupervised machine learning, we were able to resolve activated and non-activated T cell populations label free, using morphological data alone. Using a similar approach, we show label-free differentiation/classification of B cells from plasma cells with an AUC score of 0.941 and M1 and M2 polarized macrophages with an AUC score of 0.878 +/- 0.002 (n=6). Here we report results on the use of a novel, label-free cytometry platform to characterize and isolate human immune cell subsets using morphological profiling and AI with applications for investigators in basic life sciences and drug developers in small molecule, antibody, and cell therapy R&amp;D.

Label-free morphometric characterization of T cells for cell and gene therapy research and development.

Abstract Identification, characterization, and minimally invasive isolation of T cells is critical for developing cellular therapies. Here we used VisionSort, an artificial intelligence (AI)-driven cellular analysis and sorting platform, to characterize and isolate human T cell subsets with therapeutically relevant phenotypes, label-free. By capturing single-cell digital phenotypes, we characterized human T-cells and generated ‘ground truth’ functional profiles for 1) glycolysis level, 2) exhaustion state 3) activation or resting profiles and 4) viability. A set of machine-learning derived classifiers was generated to identify these phenotypic classes label-free. The classifiers showed area under the curve (AUC) performance ranges for detecting specific, phenotypically defined T-cell populations between 0.923 and 0.995. Using a combination of supervised and unsupervised machine learning approaches, we also show identification of follicular helper T (Tfh) cells from other CD4+ T cells based on morphology alone. When we analyzed the morphological profiles of T cell subsets by unsupervised machine learning using uniform maniform projection and approximation (UMAP), we found that both gamma delta (γδ) T cells were distinct from alpha beta (αβ) T cells and CD8+ T cells were distinct from CD4+ T cells. Here we report on the use of AI-based cytometry to characterize and isolate phenotypically defined T cell subsets label-free, with applications in basic life science and cell therapy R&amp;D.

Imaging vs Nonimaging Raman Spectroscopy for High-Throughput Single-Cell Phenotyping.

Raman spectroscopy can provide nonbiased single-cell analysis based on the endogenous ensemble of biomolecules, with alterations in cellular content indicative of cell state and disease. The measurements themselves can be performed in a variety of modes: generally, full imaging takes the most time but can provide the most information. By reducing the imaging resolution and generating the most characteristic single-cell Raman spectrum in the shortest time, we optimize the utility of the Raman measurement for cell phenotyping. Here, we establish methods to compare these different measurement approaches and assess what, if any, undesired effects occur in the cell. Assuming that laser-induced damage should be apparent as a change in molecular spectra across sequential measurements, and by defining the information content as the Raman-based separability of two cell lines, we thereby establish a parameter range for optimum measurement sensitivity and single-cell throughput in single-cell Raman spectroscopic analysis. While the work here uses 532 nm irradiation, the same approach can be generalized to Raman analysis at other wavelengths.

Abstract CT086: Phase I study of intravesical Fc-optimized anti-CD40 agonist antibody 2141-V11 for non-muscle invasive bladder cancer (NMIBC) unresponsive to Bacillus Calmette-Guerin (BCG) therapy

Abstract Introduction: The CD40-CD40L pathway is crucial for activating antigen-presenting cells and initiating tumor-specific T cell responses across various malignancies. However, prior agonistic anti-CD40 antibodies have encountered limited clinical success and significant toxicity. Our previous studies demonstrate that interactions between the antibody Fc domain and the inhibitory Fc-gamma receptor FcγRIIB are critical for efficient CD40 multimerization and enhanced antitumor activity. To optimize this interaction, we developed 2141-V11, a novel anti-CD40 antibody Fc-engineered to increase binding affinity for FcγRIIB, resulting in effective antitumor activity in both in vivo and in a recent first-in-human phase I trial (NCT04059588). Non-muscle invasive bladder cancer (NMIBC) is the most common presentation of bladder cancer, with a high risk of recurrence and progression despite optimal surgical interventions and standard therapies. Bacillus Calmette-Guerin (BCG) intravesical treatment has been the standard of care for NMIBC over the last five decades. However, 75% of patients ultimately become unresponsive to this therapy, leaving limited curative options aside from complete radical cystectomy. Using several preclinical bladder cancer models, including BCG-unresponsive disease, we found that intravesical administration of 2141-V11 induces durable anti-tumor immunity without systemic toxicity. Based on these findings, we initiated a Phase I study of intravesical 2141-V11 for the treatment of BCG-unresponsive NMIBC. Methods: This is an investigator-initiated Phase I, open-label, dose-escalation study to evaluate the safety and tolerability of intravesical 2141-V11 in patients with BCG-unresponsive NMIBC (N=25) who are ineligible for or decline radical cystectomy (NCT05126472). Following complete transurethral resection, intravesical 2141-V11 is administered once weekly for three doses, with re-treatment eligibility at week 13 and 25, based on disease status determined by on-treatment biopsies. The study follows an MCRM dose escalation design with five dose levels (0.7, 2.0, 7.0, 21.0 and 70.0 mg). Primary endpoints include safety and dose tolerability to determine maximal tolerated dose (MTD) and/or recommended phase 2 dose (RP2D). Secondary endpoints involve pharmacokinetic profiling and preliminary evaluation of clinical activity. Exploratory objectives include investigation of biological markers of drug activity in pre- and post-treatment tissue biopsies and urine biospecimens. As of the data cutoff on 12/20/23, 80% of the planned patients (n=20) have been enrolled, with no dose-limiting toxicities observed. Accrual completion is expected by the first quarter of 2024, with ongoing analysis of urine immune profiling, cytokine analysis, as well as single-cell spatial phenotyping in pre- and post-treatment samples. Citation Format: Juan C. Osorio, Muneeb Alam, Jeffrey Wong, David Knorr, Lucas Blanchard, Juan C. Angulo-Lozano, Karissa Whiting, Venkatraman E. Seshan, Timothy F. Donahue, Eugene K. Cha, Alvin C. Goh, Robert Smith, Guido Dalbagni, Christian Hernandez, Melissa McCarter, Eugene J. Pietzak, Jonathan E. Rosenberg, Jeffrey V. Ravetch, Bernard H. Bochner. Phase I study of intravesical Fc-optimized anti-CD40 agonist antibody 2141-V11 for non-muscle invasive bladder cancer (NMIBC) unresponsive to Bacillus Calmette-Guerin (BCG) therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT086.

Single-cell analysis of 5-aminolevulinic acid intraoperative labeling specificity for glioblastoma.

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor, and resection is a key part of the standard of care. In fluorescence-guided surgery (FGS), fluorophores differentiate tumor tissue from surrounding normal brain. The heme synthesis pathway converts 5-aminolevulinic acid (5-ALA), a fluorogenic substrate used for FGS, to fluorescent protoporphyrin IX (PpIX). The resulting fluorescence is believed to be specific to neoplastic glioma cells, but this specificity has not been examined at a single-cell level. The objective of this study was to determine the specificity with which 5-ALA labels the diversity of cell types in GBM. The authors performed single-cell optical phenotyping and expression sequencing-version 2 (SCOPE-seq2), a paired single-cell imaging and RNA sequencing method, of individual cells on human GBM surgical specimens with macroscopically visible PpIX fluorescence from patients who received 5-ALA prior to surgery. SCOPE-seq2 allowed the authors to simultaneously image PpIX fluorescence and unambiguously identify neoplastic cells from single-cell RNA sequencing. Experiments were also conducted in cell culture and co-culture models of glioma and in acute slice cultures from a mouse glioma model to investigate cell- and tissue-specific uptake and secretion of 5-ALA and PpIX. SCOPE-seq2 analysis of human GBM surgical specimens revealed that 5-ALA treatment resulted in labeling that was not specific to neoplastic glioma cells. The cell culture further demonstrated that nonneoplastic cells could be labeled by 5-ALA directly or by PpIX secreted from surrounding neoplastic cells. Acute slice cultures from mouse glioma models showed that 5-ALA preferentially labeled GBM tumor tissue over nonneoplastic brain tissue with significant labeling in the tumor margins, and that this contrast was not due to blood-brain barrier disruption. Together, these findings support the use of 5-ALA as an indicator of GBM tissue but question the main advantage of 5-ALA for specific intracellular labeling of neoplastic glioma cells in FGS. Further studies are needed to systematically compare the performance of 5-ALA to that of potential alternatives for FGS.

IKK2 controls the inflammatory potential of tissue-resident regulatory T cells in a murine gain of function model

Loss-of-function mutations have provided crucial insights into the immunoregulatory actions of Foxp3+ regulatory T cells (Tregs). By contrast, we know very little about the consequences of defects that amplify aspects of Treg function or differentiation. Here we show that mice heterozygous for an Ikbkb gain-of-function mutation develop psoriasis. Doubling the gene dose (IkbkbGoF/GoF) results in dactylitis, spondylitis, and characteristic nail changes, which are features of psoriatic arthritis. IkbkbGoF mice exhibit a selective expansion of Foxp3 + CD25+ Tregs of which a subset express IL-17. These modified Tregs are enriched in both inflamed tissues, blood and spleen, and their transfer is sufficient to induce disease without conventional T cells. Single-cell transcriptional and phenotyping analyses of isolated Tregs reveal expansion of non-lymphoid tissue (tissue-resident) Tregs expressing Th17-related genes, Helios, tissue-resident markers including CD103 and CD69, and a prominent NF-κB transcriptome. Thus, IKK2 regulates tissue-resident Treg differentiation, and overactivity drives dose-dependent skin and systemic inflammation.

Abstract 3536: Paracell: A high throughput, deep learning-based pipeline for single-cell phenotypic profiling

Abstract Variants of uncertain significance constitute the vast majority of missense variants, hampering the utility of genetic testing, despite its remarkable impacts on clinical diagnosis and decision-making. Although the sequencing and interpretation of genetic variants have been drastically improved in recent years, only about 2% of novel missense variants are currently clinically actionable. Therefore, a robust and scalable pipeline is desperately needed to provide functional evidence critical for variant interpretation. In this work, we demonstrate Paracell, a deep learning-based phenotypic profiling pipeline that leverages subcellular segmentation, high-dimensional single-cell phenotyping, and machine learning to classify the functional impact of variants. Paracell captures heterogeneity in cellular phenotypes and protein colocalization between variants and their signaling partners, as well as subcellular markers, and enables analysis at a single-cell resolution. Using a classification model trained on single-cell features extracted by Paracell, we were able to accurately classify cells from loss-of-function (LoF) variants based on their impact. After aggregating cellular profiles on a variant level, our pipeline was able to distinguish LoF variants from functional ones with high sensitivity and specificity. Our work demonstrates that Paracell is a robust and scalable method that can sensitively detect differences in single-cell phenotypic profiles. The systematic application of this pipeline will provide valuable functional evidence for variant interpretation, enhancing their clinical utility and accelerating personalized cancer care. Citation Format: David L. Nguyen, Jesse T. Chao. Paracell: A high throughput, deep learning-based pipeline for single-cell phenotypic profiling [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 3536.