Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories

Deciphering Tumor Cell Evolution in Cutaneous T-Cell Lymphomas: Distinct Differentiation Trajectories in Mycosis Fungoides and Sézary Syndrome

Cutaneous T-cell lymphomas are a heterogeneous group of neoplasms originating in the skin, with mycosis fungoides (MF) and Sézary syndrome (SS) representing the most common variants. The cellular origin of cutaneous lymphomas has remained controversial owing to their immense phenotypic heterogeneity that obfuscates lineage reconstruction on the basis of classical surface biomarkers. To overcome this heterogeneity and reconstruct the differentiation trajectory of malignant cells in MF and SS, TCR sequencing was performed in parallel with targeted transcriptomics at the single-cell resolution among cutaneous samples in MF and SS. Unsupervised lineage reconstruction showed that Sézary cells exist as a population of CD4+ T cells distinct from those in patch, plaque, and tumor MF. Further investigation of malignant cell heterogeneity in SS showed that Sézary cells phenotypically comprised at least 3 subsets on the basis of differential proliferation potentials and expression of exhaustion markers. A T helper 1-polarized cell type, intermediate cell type, and exhausted T helper 2-polarized cell type were identified, with T helper 1- and T helper 2-polarized cells displaying divergent proliferation potentials. Collectively, these findings provide evidence to clarify the relationship between MF and SS and reveal cell subsets in SS that suggest a possible mechanism for therapeutic resistance.

Basal-epithelial subpopulations underlie and predict chemotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, characterized by extensive intratumoral heterogeneity, high metastasis, and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of these aggressive behaviors remains poorly understood. Using single-cell and spatial transcriptome analysis, here we discovered basal epithelial subpopulations located within the stroma that exhibit chemoresistance characteristics. The subpopulations are defined by distinct signature genes that show a frequent gain in copy number and exhibit an activated epithelial-to-mesenchymal transition program. A subset of these genes can accurately predict chemotherapy response and are associated with poor prognosis. Interestingly, among these genes, elevated ITGB1 participates in enhancing intercellular signaling while ACTN1 confers a survival advantage to foster chemoresistance. Furthermore, by subjecting the transcriptional signatures to drug repurposing analysis, we find that chemoresistant tumors may benefit from distinct inhibitors in treatment-naive versus post-NAC patients. These findings shed light on the mechanistic basis of chemoresistance while providing the best-in-class biomarker to predict chemotherapy response and alternate therapeutic avenues for improved management of TNBC patients resistant to chemotherapy.

Long-read single-cell RNA sequencing enables the study of cancer subclone-specific genotype and phenotype in chronic lymphocytic leukemia

Bruton's tyrosine kinase (BTK) inhibitors are effective for the treatment of chronic lymphocytic leukemia (CLL) due to BTK's role in B cell survival and proliferation. Treatment resistance is most commonly caused by the emergence of the hallmark BTKC481S mutation that inhibits drug binding. In this study, we aimed to investigate whether the presence of additional CLL driver mutations in cancer subclones harboring a BTKC481S mutation accelerates subclone expansion. In addition, we sought to determine whether BTK-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we employ our recently published method (Qiao et al. 2024) that combines bulk DNA sequencing and single-cell RNA sequencing (scRNA-seq) data to genotype individual cells for the presence or absence of subclone-defining mutations. While the most common approach for scRNA-seq includes short-read sequencing, transcript coverage is limited due to the vast majority of the reads being concentrated at the priming end of the transcript. Here, we utilized MAS-seq, a long-read scRNAseq technology, to substantially increase transcript coverage across the entire length of the transcripts and expand the set of informative mutations to link cells to cancer subclones in six CLL patients who acquired BTKC481S mutations during BTK inhibitor treatment. We found that BTK-mutated subclones often acquire additional mutations in CLL driver genes, leading to faster subclone proliferation. When examining subclone-specific gene expression, we found that in one patient, BTK-mutated subclones are transcriptionally distinct from the rest of the malignant B cell population with an overexpression of CLL-relevant genes.

Highlights of how single-cell analyses are illuminating differentiation and disease in the gastric corpus

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique to identify novel cell markers, developmental trajectories, and transcriptional changes during cell differentiation and disease onset and progression. In this review, we highlight recent scRNA-seq studies of the gastric corpus in both human and murine systems that have provided insight into gastric organogenesis, identified novel markers for the various gastric lineages during development and in adults, and revealed transcriptional changes during regeneration and tumorigenesis. Overall, by elucidating transcriptional states and fluctuations at the cellular level in healthy and disease contexts, scRNA-seq may lead to better, more personalized clinical treatments for disease progression.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes

Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer. Here, we perform single-cell CRISPRi screens of 3,512 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of >500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of variant-associated enhancers disrupts breast cancer gene programs. We observe variant-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple variant-associated enhancers indirectly regulate TP53. Comparative studies illustrate sub-type specific functions between enhancers in ER+ and ER- cells. Finally, we developed the pySpade package to facilitate analysis of single-cell enhancer screens. Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture

Background

Reproducibility of human cortical organoid (hCO) phenotypes remains a concern for modeling neurodevelopmental disorders. While guided hCO protocols reproducibly generate cortical cell types in multiple cell lines at one site, variability across sites using a harmonized protocol has not yet been evaluated. We present an hCO cross-site reproducibility study examining multiple phenotypes.

Methods

Three independent research groups generated hCOs from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol. scRNA-seq, 3D fluorescent imaging, phase contrast imaging, qPCR, and flow cytometry were used to characterize the 3 month differentiations across sites.

Results

In all sites, hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions with moderate to high fidelity to the in vivo brain that were consistently organized in cortical wall-like buds. Cross-site differences were detected in hCO size and morphology. Differential gene expression showed differences in metabolism and cellular stress across sites. Although iPSC culture conditions were consistent and iPSCs remained undifferentiated, primed stem cell marker expression prior to differentiation correlated with cell type proportions in hCOs.

Conclusions

We identified hCO phenotypes that are reproducible across sites using a harmonized differentiation protocol. Previously described limitations of hCO models were also reproduced including off-target differentiations, necrotic cores, and cellular stress. Improving our understanding of how stem cell states influence early hCO cell types may increase reliability of hCO differentiations. Cross-site reproducibility of hCO cell type proportions and organization lays the foundation for future collaborative prospective meta-analytic studies modeling neurodevelopmental disorders in hCOs.

Breaking enhancers to gain insights into developmental defects

Despite ground-breaking genetic studies that have identified thousands of risk variants for developmental diseases, how these variants lead to molecular and cellular phenotypes remains a gap in knowledge. Many of these variants are non-coding and occur at enhancers, which orchestrate key regulatory programs during development. The prevailing paradigm is that non-coding variants alter the activity of enhancers, impacting gene expression programs, and ultimately contributing to disease risk. A key obstacle to progress is the systematic functional characterization of non-coding variants at scale, especially since enhancer activity is highly specific to cell type and developmental stage. Here, we review the foundational studies of enhancers in developmental disease and current genomic approaches to functionally characterize developmental enhancers and their variants at scale. In the coming decade, we anticipate systematic enhancer perturbation studies to link non-coding variants to molecular mechanisms, changes in cell state, and disease phenotypes.

Dissection and Integration of Bursty Transcriptional Dynamics for Complex Systems

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-seq data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed a novel approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our novel use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Long-read single-cell RNA sequencing (scRNA-seq) enables the quantification of RNA isoforms in individual cells. However, long-read scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes. We introduce BLAZE, which accurately and efficiently identifies 10x cell barcodes using only nanopore long-read scRNA-seq data. BLAZE outperforms the existing tools and provides an accurate representation of the cells present in long-read scRNA-seq when compared to matched short reads. BLAZE simplifies long-read scRNA-seq while improving the results, is compatible with downstream tools accepting a cell barcode file, and is available at https://github.com/shimlab/BLAZE .

Complexities of mammalian transcriptome revealed by targeted RNA enrichment techniques

Studies using highly sensitive targeted RNA enrichment methods have shown that a large portion of the human transcriptome remains to be discovered and that most of the genome is transcribed in a complex, interleaved fashion characterized by a complex web of transcripts emanating from protein coding and noncoding loci. These results resonate with those from single-cell transcriptome profiling endeavors that reveal the existence of multiple novel, cell type-specific transcripts and clearly demonstrate that our understanding of the complexities of the human transcriptome is far from being complete. Here, we review the current status of the targeted RNA enrichment techniques, their application to the discovery of novel cell type-specific transcripts, and their impact on our understanding of the human genome and transcriptome.

Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers

MOTIVATION

Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during polymerase chain reaction (PCR) and sequencing. One solution attaches unique molecular identifiers (UMIs) to sample sequences before amplification. Counting UMIs instead of sequences provides unbiased estimates of abundance. While modern methods improve over naïve counting by UMI identity, most do not account for UMI reuse or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences.

RESULTS

We introduce Deduplication and Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological amplicon sequences and accurately estimate their deduplicated abundance. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/DormanLab/AmpliCI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Density-based detection of cell transition states to construct disparate and bifurcating trajectories

Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.

Single-cell transcriptome profiling reveals several LncRNAs differentially expressed in idiopathic germ cell aplasia

Mechanisms underlying severe male infertility are still largely elusive. However, recently, a single-cell transcription study by our group identified several differentially expressed coding genes in all the somatic cell types in testes of patients with idiopathic germ cell aplasia (iGCA). Here, we leverage this work by extending the analysis also to the non-coding portion of the genome. As a result, we found that 43 LncRNAs were differentially expressed in the somatic cells of these patients. Interestingly, a significant portion of the overexpressed LncRNAs was found to be a target of TAF9B, a transcription factor known to be involved in germ cell survival. Moreover, several overexpressed LncRNAs were also found to be activated in a mouse model of Sertoli cells treated with bisphenol A, a widespread environmental contaminant, long suspected to impair male fertility. Finally, a literature search for MEG3, a maternally imprinted LncRNA overexpressed as well in our patients, found it to be involved, among other things, in obesity and inflammation, known comorbidities of iGCA, ultimately suggesting that our findings deepen the understanding of the molecular insights coupled not only to the pathogenesis, but also to the clinical course of this class of patients.

Phenotypic and Transcriptomic Responses to Stress Differ According to Population Geography in an Invasive Species

Adaptation to rapid environmental changes must occur within a short-time scale. In this context, studies of invasive species may provide insights into the underlying mechanisms of rapid adaptation as these species have repeatedly encountered and adapted to novel environmental conditions. We investigated how invasive and noninvasive genotypes of Drosophila suzukii deal with oxidative stress at the phenotypic and molecular levels. We also studied the impact of transposable element (TE) insertions on the gene expression in response to stress. Our results show that flies from invasive areas (France and the United States) live longer in natural conditions than the ones from native Japanese areas. As expected, lifespan for all genotypes was significantly reduced following exposure to paraquat, but this reduction varied among genotypes (genotype-by-environment interaction) with invasive genotypes appearing more affected by exposure than noninvasive ones. A transcriptomic analysis of genotypes upon paraquat treatment detected many genes differentially expressed (DE). Although a small core set of genes were DE in all genotypes following paraquat exposure, much of the response of each genotype was unique. Moreover, we showed that TEs were not activated after oxidative stress and DE genes were significantly depleted of TEs. In conclusion, it is likely that transcriptomic changes are involved in the rapid adaptation to local environments. We provide new evidence that in the decade since the invasion from Asia, the sampled genotypes in Europe and the United States of D. suzukii diverged from the ones from the native area regarding their phenotypic and genomic response to oxidative stress.