Mapping single-cell transcriptomes to copy number evolutionary trees

Cancer arises and evolves by the accumulation of somatic mutations that provide a selective advantage. The interplay of mutations and their functional consequences shape the evolutionary dynamics of tumors and contribute to different clinical outcomes. In the absence of scalable methods to jointly assay genomic and transcriptomic profiles of the same individual cell, the two data modalities are usually measured separately and need to be integrated computationally. Here, we introduce SCATrEx, a statistical model to map single-cell gene expression data onto the evolutionary history of copy number alterations of the tumor. SCATrEx jointly assigns cancer cells assayed with scRNA-seq to copy number profiles arranged in a copy number aberration tree and augments the tree with clone-specific clusters. Our simulations show that SCATrEx improves over both state-of-the-art unsupervised clustering methods and cell-to-clone assignment methods. In an application to real data, we observe that SCATrEx finds inter-clone and intra-clone gene expression heterogeneity not detectable using other integration methods. SCATrEx will allow for a better understanding of tumor evolution by jointly analysing the genomic and transcriptomic changes that drive it.

Functional Orderly Topography of Brain Networks Associated with Gene Expression Heterogeneity

The human cerebral cortex expanded much more relative to non-human primates and rodent in evolution, leading to a functional orderly topography of the brain networks. Here, we show that functional topography may be associated with gene expression heterogeneity in various brain structures. The neocortex exhibits greater gene expression heterogeneity, with lower housekeeping gene proportion, a longer mean path length, less clusters, and a lower degree of ordering of networks, compared to archicortical and subcortical area in human, rhesus macaque, and mouse brains consistently. In particular, the cerebellar cortex displays greater gene expression heterogeneity than cerebellar deep nuclei in the human brain, but not in the mouse brain, corresponding to the emergence of novel functions in the human cerebellar cortex. Moreover, the cortical areas with greater gene expression heterogeneity, primarily located in multimodal association cortex, tend to express genes with higher evolutionary rates and exhibit higher functional connectivity degree measured by resting-state fMRI, implying that such spatial pattern of cortical gene expression may be shaped by evolution and favorable for the specialization of higher cognitive functions. Together, the cross-species imaging genetic findings may provide convergent evidence to support the association between the orderly topography of brain function networks and gene expression.

Designing single-cell experiments to harvest fluctuation noise while rejecting measurement noise

Measurement error is a complicating factor that could reduce or distort the information contained in an experiment. This problem becomes even more serious in the context of experiments to measure single-cell gene expression heterogeneity, in which important quantities such as RNA and protein copy numbers are themselves subjected to the inherent randomness of biochemical reactions. Yet, it is not clear how measurement noise should be managed, in addition to other experiment design variables such as sampling size and frequency, in order to ensure that the collected data provides useful insights on the gene expression mechanism of interest. To address these experiment design challenges, we propose a model-centric framework that makes explicit use of measurement error modeling and Fisher Information Matrix-based criteria to decide between experimental methods. This unified approach not only allows us to see how different noise characteristics affect uncertainty in parameter estimation, but also enables a systematic approach to designing hybrid experiments that combine different measurement methods.

Nuclei are mobile processors enabling specialization in a gigantic single-celled syncytium

In multicellular organisms, the specification, coordination, and compartmentalization of cell types enable the formation of complex body plans. However, some eukaryotic protists such as slime molds generate diverse and complex structures while remaining in a multinucleated syncytial state. It is unknown if different regions of these giant syncytial cells have distinct transcriptional responses to environmental encounters, and if nuclei within the cell diversify into heterogeneous states. Here we performed spatial transcriptome analysis of the slime mold Physarum polycephalum in the plasmodium state under different environmental conditions, and used single-nucleus RNA-sequencing to dissect gene expression heterogeneity among nuclei. Our data identifies transcriptome regionality in the organism that associates with proliferation, syncytial substructures, and localized environmental conditions. Further, we find that nuclei are heterogenous in their transcriptional profile, and may process local signals within the plasmodium to coordinate cell growth, metabolism, and reproduction. To understand how nuclei variation within the syncytium compares to heterogeneity in single-nucleated cells, we analyzed states in single Physarum amoebal cells. We observed amoebal cell states at different stages of mitosis and meiosis, and identified cytokinetic features that are specific to nuclei divisions within the syncytium. Notably, we do not find evidence for predefined transcriptomic states in the amoebae that are observed in the syncytium. Our data shows that a single-celled slime mold can control its gene expression in a region-specific manner while lacking cellular compartmentalization, and suggests that nuclei are mobile processors facilitating local specialized functions. More broadly, slime molds offer the extraordinary opportunity to explore how organisms can evolve regulatory mechanisms to divide labor, specialize, balance competition with cooperation, and perform other foundational principles that govern the logic of life.

Spatial transcriptome sequencing revealed spatial trajectory in the Non-Small Cell Lung Carcinoma

Deepening understanding in the heterogeneity of tumors is critical for clinical treatment. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal lung cancer using the spatial transcriptomics (ST) technology. We identified gene expression gradients in stroma adjacent to tumor regions that allow for re-understanding of the tumor micro-environment. The establishment of these profiles was the first step towards an unbiased view of lung cancer and can serve as a dictionary for future studies. Tumor subclones were detected by ST technology in our research, while we contrast the EMT ability among in subclones which inferred the possible trajectory of tumor metastasis and invasion, which was confirmed by constructing the pseudo-time model of spatial transition within subclones. Together, these results uncovered lung cancer spatial heterogeneity, highlight potential tumor micro-environment differences and spatial evolution trajectory, and served as a resource for further investigation of tumor microenvironment.

Contribution of DNA adenine methylation to gene expression heterogeneity in Salmonella enterica

Expression of Salmonella enterica loci harboring undermethylated GATC sites at promoters or regulatory regions was monitored by single cell analysis. Cell-to-cell differences in expression were detected in ten such loci (carA, dgoR, holA, nanA, ssaN, STM1290, STM3276, STM5308, gtr and opvAB), with concomitant formation of ON and OFF subpopulations. The ON and OFF subpopulation sizes varied depending on the growth conditions, suggesting that the population structure can be modulated by environmental control. All the loci under study except STM5308 displayed altered patterns of expression in strains lacking or overproducing Dam methylase, thereby confirming control by Dam methylation. Bioinformatic analysis identified potential binding sites for transcription factors OxyR, CRP and Fur, and analysis of expression in mutant backgrounds confirmed transcriptional control by one or more of such factors. Surveys of gene expression in pairwise combinations of Dam methylation-dependent loci revealed independent switching, thus predicting the formation of a high number of cell variants. This study expands the list of S. enterica loci under transcriptional control by Dam methylation, and underscores the relevance of the DNA adenine methylome as a source of phenotypic heterogeneity.

Validation of the Nanostring of the Banff Human Organ Transplant Gene Panel in Archival Data from Human Kidney Transplants and its Modelling Complexity

Background RNA gene expression of renal transplantation biopsies is commonly used to identify rejection. Mostly done with microarrays, seminal findings describe and define the patterns of genes associated with types of rejection and non-rejection kidney allograft diagnoses. To make gene expression more accessible for pathology laboratories, the Molecular Diagnostics Working Group of the Banff Foundation for Allograft Pathology and NanoString Technologies partnered to create the Banff Human Organ Transplant Panel (BHOT), a gene panel set of 770 genes as a substitute for microarrays (~ 50,000 genes). The advantage of this platform is that gene expressions are quantifiable on formalin fixed and paraffin embedded archival tissue samples. This new technology, thus, makes gene expressions cheaper and accessible to more laboratories and investigators. The purpose of this report is to validate the BHOT panel as a surrogate for microarrays and test the accuracy of the modelled BHOT data. Results This limited NanoString gene set readily identifies renal rejection and non-rejection diagnostic patterns using in silico statistical analyses of seminal archival databases derived from renal transplant RNA expression arrays. Multiple modelling algorithms show a highly variable pattern within the error matrices per sample. The discrepancies within the error matrices are most likely related to the gene expression heterogeneity of samples within a given pathological diagnosis. This was confirmed by clustering the data into 8 groups, which modelled with fewer misclassifications. Conclusion This report validates gene expression of human renal allografts using the Banff Human Organ Transplant Panel as a surrogate for microarrays and confirms the its modelling complexity.