Single-cell trajectory analysis reveals a CD9 positive state to contribute to exit from stem cell-like and embryonic diapause states and transit to drug-resistant states

Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines

Introduction

Colorectal cancer (CRC) is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date, chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for the treatment of metastatic colorectal cancer are 5-fluorouracil, oxaliplatin and irinotecan which is metabolized to an active compound SN-38. The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct a predictive gene expression-based classifier to separate responders and non-responders.

Methods

In this study, we analyzed gene expression profiles of seven patient-derived CRC organoids and performed correlation analyses between gene expression and IC50 values for the three standard-of-care chemotherapeutic drugs. We also included in the study publicly available datasets of colorectal cancer cell lines, thus combining two different in vitro models relevant to cancer research. Logistic regression was used to build gene expression-based classifiers for metastatic Stage IV and non-metastatic Stage II/III CRC patients. Prognostic performance was evaluated through Kaplan-Meier survival analysis and log-rank tests, while independent prognostic significance was assessed using multivariate Cox proportional hazards modeling.

Results

A small set of genes showed consistent correlation with resistance to chemotherapy across different datasets. While some genes were previously implicated in cancer prognosis and drug response, several were linked to drug resistance for the first time. The resulting gene expression signatures successfully stratified Stage II/III and Stage IV CRC patients, with potential clinical utility for improving treatment outcomes after further validation.

Discussion

This study highlights the advantages of integrating diverse experimental models, such as organoids and cell lines, to identify novel prognostic biomarkers and enhance the understanding of chemotherapy resistance in CRC.

Multiparametric profiling of HER2-enriched extracellular vesicles in breast cancer using Single Extracellular VEsicle Nanoscopy

BACKGROUND

Patients with HER2-positive breast cancer can significantly benefit from HER2-directed therapy - such as the monoclonal antibody trastuzumab. However, some patients can develop therapy resistance or change HER2 status. Thus, we urgently need new, noninvasive strategies to monitor patients frequently. Extracellular vesicles (EVs) secreted from tumor cells are emerging as potential biomarker candidates. These membrane-delimited nanoparticles harbor molecular signatures of their origin cells; report rapidly on changes to cellular status; and can be frequently sampled from accessible biofluids.

RESULTS

Using Single Extracellular VEsicle Nanoscopy (SEVEN) platform that combines affinity isolation of EVs with super-resolution microscopy, here we provide multiparametric characterization of EVs with ~ 8 nm precision and molecular sensitivity. We first interrogated cell culture EVs affinity-enriched in tetraspanins CD9, CD63, and CD81; these transmembrane proteins are commonly found on EV membranes. SEVEN robustly provided critical parameters of individual, tetraspanin-enriched EVs: concentration, size, shape, molecular cargo content, and heterogeneity. Trastuzumab-resistant cells (vs. trastuzumab-sensitive) secreted more EVs. Additionally, EVs from trastuzumab-resistant cells had lower tetraspanin density and higher HER2 density. We also evaluated EVs affinity-enriched in HER2; we found that these EVs (vs. tetraspanin-enriched) were larger and more elongated. We further optimized analytical sample processing to assess a rare population of HER2-enriched EVs from patient plasma. In breast cancer patients with elevated HER2 protein expression (vs. controls), HER2-enriched EVs had distinct characteristics including typically increased number of tetraspanin molecules and larger size. Importantly, these EVs were on average 25-fold more abundant compared to no cancer controls.

CONCLUSIONS

SEVEN revealed unique characteristics of HER2-enriched EVs in cultured cells and complex biological fluid. In combination with current clinical approaches, this method is well poised to support precise therapeutic decisions.

Single-cell RNA sequencing reveals the transcriptional heterogeneity of Tbx18-positive cardiac cells during heart development

The T-box family transcription factor 18 (Tbx18) has been found to play a critical role in regulating the development of the mammalian heart during the primary stages of embryonic development while the cellular heterogeneity and landscape of Tbx18-positive (Tbx18+) cardiac cells remain incompletely characterized. Here, we analyzed prior published single-cell RNA sequencing (scRNA-seq) mouse heart data to explore the heterogeneity of Tbx18+ cardiac cell subpopulations and provide a comprehensive transcriptional landscape of Tbx18+ cardiac cells during their development. Bioinformatic analysis methods were utilized to identify the heterogeneity between cell groups. Based on the gene expression characteristics, Tbx18+ cardiac cells can be classified into a minimum of two distinct cell populations, namely fibroblast-like cells and cardiomyocytes. In terms of temporal heterogeneity, these cells exhibit three developmental stages, namely the MEM stage, ML_P0 stage, and P stage Tbx18+ cardiac cells. Furthermore, Tbx18+ cardiac cells encompass several cell types, including cardiac progenitor-like cells, cardiomyocytes, and epicardial/stromal cells, as determined by specific transcriptional regulatory networks. The scRNA-seq results revealed the involvement of extracellular matrix (ECM) signals and epicardial epithelial-to-mesenchymal transition (EMT) in the development of Tbx18+ cardiac cells. The utilization of a lineage-tracing model served to validate the crucial function of Tbx18 in the differentiation of cardiac cells. Consequently, these findings offer a comprehensive depiction of the cellular heterogeneity within Tbx18+ cardiac cells.

Computational single cell oncology: state of the art

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