Dynamic thresholding and tissue dissociation optimization for CITE-seq identifies differential surface protein abundance in metastatic melanoma

Multi-omics profiling by CITE-seq bridges the RNA-protein gap in single-cell analysis but has been largely applied to liquid biopsies. Applying CITE-seq to clinically relevant solid biopsies to characterize healthy tissue and the tumor microenvironment is an essential next step in single-cell translational studies. In this study, gating of cell populations based on their transcriptome signatures for use in cell type-specific ridge plots allowed identification of positive antibody signals and setting of manual thresholds. Next, we compare five skin dissociation protocols by taking into account dissociation efficiency, captured cell type heterogeneity and recovered surface proteome. To assess the effect of enzymatic digestion on transcriptome and epitope expression in immune cell populations, we analyze peripheral blood mononuclear cells (PBMCs) with and without dissociation. To further assess the RNA-protein gap, RNA-protein we perform codetection and correlation analyses on thresholded protein values. Finally, in a proof-of-concept study, using protein abundance analysis on selected surface markers in a cohort of healthy skin, primary, and metastatic melanoma we identify CD56 surface marker expression on metastatic melanoma cells, which was further confirmed by multiplex immunohistochemistry. This work provides practical guidelines for processing and analysis of clinically relevant solid tissue biopsies for biomarker discovery.

gExcite - A start-to-end framework for single-cell gene expression, hashing, and antibody analysis

SUMMARY

Recently, CITE-seq emerged as a multimodal single-cell technology capturing gene expression and surface protein information from the same single-cells, which allows unprecedented insights into disease mechanisms and heterogeneity, as well as immune cell profiling. Multiple single-cell profiling methods exist, but they are typically focussed on either gene expression or antibody analysis, not their combination. Moreover, existing software suites are not easily scalable to a multitude of samples. To this end, we designed gExcite, a start-to-end workflow that provides both gene and antibody expression analysis, as well as hashing deconvolution. Embedded in the Snakemake workflow manager, gExcite facilitates reproducible and scalable analyses. We showcase the output of gExcite on a study of different dissociation protocols on PBMC samples.

AVAILABILITY

gExcite is open source available on github at https://github.com/ETH-NEXUS/gExcite_pipeline. The software is distributed under the GNU General Public License 3 (GPL3).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Abstract 5926: High dimensional morphology analysis reveals new insights in melanoma cell heterogeneity

Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity within the tumors. These characteristics have made a deeper understanding of melanomas challenging. Classically, melanoma cells are characterized with a limited set of protein biomarkers. Gene expression signatures and mutational analysis (e.g., BRAF and NRAS genotyping) can provide a more detailed view of heterogeneity but may not translate to readily available biomarkers for functional studies. The Deepcell platform enables multi-dimensional morphology analysis and enrichment of unlabeled single cells using artificial intelligence (AI), advanced imaging, and microfluidics, enabling high resolution profiling of population heterogeneity. Label-free multi-dimensional morphology analysis may have higher resolution than a limited set of protein biomarkers, minimizes perturbation to the transcriptome, and reduces cell handling steps. We used patient-derived cell lines and dissociated biopsy samples to train a Deepcell AI model to identify and sort for melanoma cells based on morphology alone. The model was tested on metastatic melanoma biopsies, with identification and enrichment of melanoma cells verified by various downstream assays, including scRNASeq. In addition to melanoma populations, the AI model classified various cells of the microenvironment, such as stromal cells and immune subtypes, based on morphology only. To further characterize tumor heterogeneity, we imaged >25 patient-derived melanoma cell lines representing melanocytic, mesenchymal, and intermediate phenotypic states on the Deepcell platform. Morphology analysis of these images revealed distinct clusters of cells for each phenotype, indicating that there are morphological differences associated with each state. We developed a random forest (RF) classifier to identify the top differential morphological features between the different cell lines, thereby providing a label-free means of phenotyping melanoma samples. The morphology analysis of the cell lines uncovered significant variability in pigmentation; an RF classifier distinguished between pigmented vs non-pigmented cells with >90% accuracy. Pigmentation is a hallmark of melanoma cells, and it has been associated with the melanocytic phenotype and differential drug response in vitro. However, there is not currently a robust method to profile and study pigmentation in live cells. We further investigated this observation by correlating morphological profiles, molecular, and functional information with the level of cell pigmentation. The Deepcell platform presents a new method for sorting and characterizing cellular heterogeneity using morphology, including pigmentation status. As such, multi-dimensional morphology analysis will bolster the understanding of complex melanoma states and tumor microenvironment, particularly in patient derived biopsies.

Citation Format: Evelyn Lattmann, Aizhan Tastanova, Andreja Jovic, Kiran Saini, Tiffine Pham, Christian Corona, Jeanette Mei, Michael Phelan, Stephane C. Boutet, Ryan Carelli, Kevin B. Jacobs, Julie Kim, Manisha Ray, Chassidy Johnson, Nianzhen Li, Mahyar Salek, Maddison Masaeli, Mitchell P. Levesque. High dimensional morphology analysis reveals new insights in melanoma cell heterogeneity [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 5926.

ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma

Clinical management of melanomas with NRAS mutations is challenging. Targeting MAPK signaling is only beneficial to a small subset of patients due to resistance that arises through genetic, transcriptional, and metabolic adaptation. Identification of targetable vulnerabilities in NRAS-mutated melanoma could help improve patient treatment. Here, we used multiomics analyses to reveal that NRAS-mutated melanoma cells adopt a mesenchymal phenotype with a quiescent metabolic program to resist cellular stress induced by MEK inhibition. The metabolic alterations elevated baseline reactive oxygen species (ROS) levels, leading these cells to become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intratumor heterogeneity necessitates the combination of a ROS inducer and a MEK inhibitor to inhibit both tumor growth and metastasis. Ex vivo pharmacoscopy of 62 human metastatic melanomas confirmed that MEK inhibitor-resistant tumors significantly benefited from the combination therapy. Finally, oxidative stress response and translational suppression corresponded with ROS-inducer sensitivity in 486 cancer cell lines, independent of cancer type. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS inducers in melanoma and other cancers.

SIGNIFICANCE

Metabolic reprogramming in drug-resistant NRAS-mutated melanoma cells confers sensitivity to ROS induction, which suppresses tumor growth and metastasis in combination with MAPK pathway inhibitors.

Guadecitabine increases response to combined anti-CTLA-4 and anti-PD-1 treatment in mouse melanoma in vivo by controlling T-cells, myeloid derived suppressor and NK cells

BACKGROUND

The combination of Programmed Cell Death 1 (PD-1) and Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) blockade has dramatically improved the overall survival rate for malignant melanoma. Immune checkpoint blockers (ICBs) limit the tumor's immune escape yet only for approximately a third of all tumors and, in most cases, for a limited amount of time. Several approaches to overcome resistance to ICBs are being investigated among which the addition of epigenetic drugs that are expected to act on both immune and tumor cells. Guadecitabine, a dinucleotide prodrug of a decitabine linked via phosphodiester bond to a guanosine, showed promising results in the phase-1 clinical trial, NIBIT-M4 (NCT02608437).

METHODS

We used the syngeneic B16F10 murine melanoma model to study the effects of immune checkpoint blocking antibodies against CTLA-4 and PD-1 in combination, with and without the addition of Guadecitabine. We comprehensively characterized the tumor's and the host's responses under different treatments by flow cytometry, multiplex immunofluorescence and methylation analysis.

RESULTS

In combination with ICBs, Guadecitabine significantly reduced subcutaneous tumor growth as well as metastases formation compared to ICBs and Guadecitabine treatment. In particular, Guadecitabine greatly enhanced the efficacy of combined ICBs by increasing effector memory CD8+ T cells, inducing effector NK cells in the spleen and reducing tumor infiltrating regulatory T cells and myeloid derived suppressor cells (MDSC), in the tumor microenvironment (TME). Guadecitabine in association with ICBs increased serum levels of IFN-γ and IFN-γ-induced chemokines with anti-angiogenic activity. Guadecitabine led to a general DNA-demethylation, in particular of sites of intermediate methylation levels.

CONCLUSIONS

These results indicate Guadecitabine as a promising epigenetic drug to be added to ICBs therapy.

An Optimized Tissue Dissociation Protocol for Single-Cell RNA Sequencing Analysis of Fresh and Cultured Human Skin Biopsies

We present an optimized dissociation protocol for preparing high-quality skin cell suspensions for in-depth single-cell RNA-sequencing (scRNA-seq) analysis of fresh and cultured human skin. Our protocol enabled the isolation of a consistently high number of highly viable skin cells from small freshly dissociated punch skin biopsies, which we use for scRNA-seq studies. We recapitulated not only the main cell populations of existing single-cell skin atlases, but also identified rare cell populations, such as mast cells. Furthermore, we effectively isolated highly viable single cells from ex vivo cultured skin biopsy fragments and generated a global single-cell map of the explanted human skin. The quality metrics of the generated scRNA-seq datasets were comparable between freshly dissociated and cultured skin. Overall, by enabling efficient cell isolation and comprehensive cell mapping, our skin dissociation-scRNA-seq workflow can greatly facilitate scRNA-seq discoveries across diverse human skin pathologies and ex vivo skin explant experimentations.

A Comparative Study of Real-Time RT-PCR-Based SARS-CoV-2 Detection Methods and Its Application to Human-Derived and Surface Swabbed Material

Real-time RT-PCR remains a gold standard in the detection of various viral diseases. In the coronavirus 2019 pandemic, multiple RT-PCR-based tests were developed to screen for viral infection. As an emergency response to increasing testing demand, we established a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR diagnostics platform for which we compared different commercial and in-house RT-PCR protocols. Four commercial, one customized, and one in-house RT-PCR protocols were evaluated with 92 SARS-CoV-2-positive and 92 SARS-CoV-2-negative samples. Furthermore, economical and practical characteristics of these protocols were compared. In addition, a highly sensitive digital droplet PCR (ddPCR) method was developed, and application of RT-PCR and ddPCR methods on SARS-CoV-2 environmental samples was examined. Very low limits of detection (1 or 2 viral copies/μL), high sensitivities (93.6% to 97.8%), and high specificities (98.7% to 100%) for the tested RT-PCR protocols were found. Furthermore, the feasibility of downscaling two of the commercial protocols, which could optimize testing capacity, was demonstrated. Tested commercial and customized RT-PCR detection kits show very good and comparable sensitivity and specificity, and the kits could be further optimized for use on SARS-CoV-2 viral samples derived from human and surface swabbed samples.

Collection and preprocessing of fine needle aspirate patient samples for single cell profiling and data analysis

High cell viability and recovered cell concentration are typical quality control requirements for single-cell processing and quality data. This protocol describes procedures for sampling, live-cell biobanking, preprocessing for single-cell RNA sequencing, and analysis of fine-needle aspiration (FNA) samples of the skin. The minimally invasive nature of FNA collection is more accepted by patients and allows for frequent longitudinal sampling, resulting in high-quality single-cell sequencing data that capture cellular heterogeneity in clinical samples.

Prolonged Unfrozen Storage and Repeated Freeze-Thawing of SARS-CoV-2 Patient Samples Have Minor Effects on SARS-CoV-2 Detectability by RT-PCR

Reliable transportation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patient samples from a swabbing station to a diagnostics facility is essential for accurate results. Therefore, cooling or freezing the samples is recommended in case of longer transportation times. In this study, SARS-CoV-2 detectability by RT-PCR was assessed after prolonged unfrozen storage or repetitive freeze-thawing of SARS-CoV-2 samples. SARS-CoV-2–positive patient swabs stored in viral transport medium were exposed to different temperatures (4°C, 25°C, and 35°C) and to repetitive freeze-thawing, to assess the effect of storage conditions on RT-PCR detection. SARS-CoV-2 RNA was still reliably detected by RT-PCR after 21 days of storage in viral transport medium, even when the samples had been stored at 35°C. The maximum observed change in cycle threshold value per day was 0.046 (±0.019) at 35°C, and the maximum observed change in cycle threshold value per freeze-thaw cycle per day was 0.197 (±0.06). Compared with storage at 4°C, viral RNA levels deviated little but significantly when stored at 25°C or 35°C, or after repeated freeze-thawing. The results of this study indicate that viral RNA levels are relatively stable at higher temperatures and repetitive freeze-thawing.

Oncolytic virotherapy-mediated anti-tumor response: a single-cell perspective

Talimogene laherparepvec (T-VEC) is a genetically modified herpes simplex 1 virus (HSV-1) approved for cancer therapy. We investigate its effect on the clinical, histological, single-cell transcriptomic, and immune repertoire level using repeated fine-needle aspirates (FNAs) of injected and noninjected lesions in primary cutaneous B cell lymphoma (pCBCL). Thirteen patients received intralesional T-VEC, 11 of which demonstrate tumor response in the injected lesions. Using single-cell sequencing of the FNAs, we identify the malignant population and separate three pCBCL subtypes. Twenty-four hours after the injection, we detect HSV-1^T-VEC transcripts in malignant and nonmalignant cells of the injected lesion but not of the noninjected lesion. Oncolytic virotherapy results in a rapid eradication of malignant cells. It also leads to interferon pathway activation and early influx of natural killer cells, monocytes, and dendritic cells. These events are followed by enrichment in cytotoxic T cells and a decrease of regulatory T cells in injected and noninjected lesions.

Synthetic biology-based cellular biomedical tattoo for detection of hypercalcemia associated with cancer

Diagnosis marks the beginning of any successful therapy. Because many medical conditions progress asymptomatically over extended periods of time, their timely diagnosis remains difficult, and this adversely affects patient prognosis. Focusing on hypercalcemia associated with cancer, we aimed to develop a synthetic biology-inspired biomedical tattoo using engineered cells that would (i) monitor long-term blood calcium concentration, (ii) detect onset of mild hypercalcemia, and (iii) respond via subcutaneous accumulation of the black pigment melanin to form a visible tattoo. For this purpose, we designed cells containing an ectopically expressed calcium-sensing receptor rewired to a synthetic signaling cascade that activates expression of transgenic tyrosinase, which produces melanin in response to persistently increased blood Ca²⁺ We confirmed that the melanin-generated color change produced by this biomedical tattoo could be detected with the naked eye and optically quantified. The system was validated in wild-type mice bearing subcutaneously implanted encapsulated engineered cells. All animals inoculated with hypercalcemic breast and colon adenocarcinoma cells developed tattoos, whereas no tattoos were seen in animals inoculated with normocalcemic tumor cells. All tumor-bearing animals remained asymptomatic throughout the 38-day experimental period. Although hypercalcemia is also associated with other pathologies, our findings demonstrate that it is possible to detect hypercalcemia associated with cancer in murine models using this cell-based diagnostic strategy.