Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

A practical guide to spatial transcriptomics

Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.

TBX3 transfection and nodal signal pathway inhibition promote differentiation of adipose mesenchymal stem cell to cardiac pacemaker-like cells

BACKGROUND

Mesenchymal stem cells (MSCs) are known as one of the best candidate cells to produce cardiac pacemaker-like cells (CPLCs). Upregulation of TBX3 transcription factor and inhibition of the nodal signal pathway have a significant role in the formation of cardiac pacemaker cells such as sinoatrial and atrioventricular nodes, which initiate the heartbeat and control the rhythm of heart contractions. This study aimed to confirm the effects of transfection of TBX3 transcription factor and inhibition of the nodal signal pathway on differentiating adipose-derived MSCs (AD-MSCs) to CPLCs. AD-MSCs were characterized using flow cytometry and three-lineage differentiation staining.

METHODS

The transfection of TBX3 plasmid was carried out using lipofectamine, and inhibition of the nodal signal pathway was done using the small-molecule SB431542. The morphology of the cells was observed using a light microscope. Pacemaker-specific markers, including TBX3, Cx30, HCN4, HCN1, HCN3, and KCNN4, were evaluated using the qRT-PCR method. For protein level, TBX3 and Cx30 were evaluated using ELISA and immunofluorescence staining. The electrophysiology of cells was evaluated using a patch clamp.

RESULTS

The TBX3 expression in the TBX3, SM, and TBX + SM groups significantly higher (p < 0.05) compared to the control group and cardiomyocytes. The expression of Cx40 and Cx43 genes were lower in TBX3, SM, TBX + SM groups. In contrast, Cx30 gene showed higher expression in TBX3 group. The expression HCN1, HCN3, and HCN4 genes are higher in TBX3 group.

CONCLUSION

The transfection of TBX3 and inhibition of the nodal signal pathway by small-molecule SB431542 enhanced differentiation of AD-MSCs to CPLCs.

Lessons learned from phase 3 trials of immunotherapy for glioblastoma: Time for longitudinal sampling?

Glioblastoma (GBM)'s median overall survival is almost 21 months. Six phase 3 immunotherapy clinical trials have recently been published, yet 5/6 did not meet approval by regulatory bodies. For the sixth, approval is uncertain. Trial failures result from multiple factors, ranging from intrinsic tumor biology to clinical trial design. Understanding the clinical and basic science of these 6 trials is compelled by other immunotherapies reaching the point of advanced phase 3 clinical trial testing. We need to understand more of the science in human GBMs in early trials: the "window of opportunity" design may not be best to understand complex changes brought about by immunotherapeutic perturbations of the GBM microenvironment. The convergence of increased safety of image-guided biopsies with "multi-omics" of small cell numbers now permits longitudinal sampling of tumor and biofluids to dissect the complex temporal changes in the GBM microenvironment as a function of the immunotherapy.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

Deciphering the Biology of Circulating Tumor Cells through Single-Cell RNA Sequencing: Implications for Precision Medicine in Cancer

Circulating tumor cells (CTCs) hold unique biological characteristics that directly involve them in hematogenous dissemination. Studying CTCs systematically is technically challenging due to their extreme rarity and heterogeneity and the lack of specific markers to specify metastasis-initiating CTCs. With cutting-edge technology, single-cell RNA sequencing (scRNA-seq) provides insights into the biology of metastatic processes driven by CTCs. Transcriptomics analysis of single CTCs can decipher tumor heterogeneity and phenotypic plasticity for exploring promising novel therapeutic targets. The integrated approach provides a perspective on the mechanisms underlying tumor development and interrogates CTCs interactions with other blood cell types, particularly those of the immune system. This review aims to comprehensively describe the current study on CTC transcriptomic analysis through scRNA-seq technology. We emphasize the workflow for scRNA-seq analysis of CTCs, including enrichment, single cell isolation, and bioinformatic tools applied for this purpose. Furthermore, we elucidated the translational knowledge from the transcriptomic profile of individual CTCs and the biology of cancer metastasis for developing effective therapeutics through targeting key pathways in CTCs.