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QC 20220728

QC 20230406

Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation., QC 20201217

QC 20200311

Cutaneous malignant melanoma (melanoma) is characterized by a high mutational load, extensive intertumoral and intratumoral genetic heterogeneity, and complex tumor microenvironment (TME) interactions. Further insights into the mechanisms underlying melanoma are crucial for understanding tumor progression and responses to treatment. Here we adapted the technology of spatial transcriptomics (ST) to melanoma lymph node biopsies and successfully sequenced the transcriptomes of over 2,200 tissue domains. Deconvolution combined with traditional approaches for dimensional reduction of transcriptome-wide data enabled us to both visualize the transcriptional landscape within the tissue and identify gene expression profiles linked to specific histologic entities. Our unsupervised analysis revealed a complex spatial intratumoral composition of melanoma metastases that was not evident through morphologic annotation. Each biopsy showed distinct gene expression profiles and included examples of the coexistence of multiple melanoma signatures within a single tumor region as well as shared profiles for lymphoid tissue characterized according to their spatial location and gene expression profiles. The lymphoid area in close proximity to the tumor region displayed a specific expression pattern, which may reflect the TME, a key component to fully understanding tumor progression. In conclusion, using the ST technology to generate gene expression profiles reveals a detailed landscape of melanoma metastases. This should inspire researchers to integrate spatial information into analyses aiming to identify the factors underlying tumor progression and therapy outcome. Significance: Applying ST technology to gene expression profiling in melanoma lymph node metastases reveals a complex transcriptional landscape in a spatial context, which is essential for understanding the multiple components of tumor progression and therapy outcome. (C) 2018 AACR., QC 20181120

microRNAs (miRNAs) are small noncoding RNAs that mediate post-transcriptional gene regulation and have emerged as essential regulators of many developmental events. The transcriptional network during early embryogenesis of the purple sea urchin, Strongylocentrotus purpuratus, is well described and can serve as an excellent model to test functional contributions of miRNAs in embryogenesis. We examined the loss of function phenotypes of major components of the miRNA biogenesis pathway. Inhibition of de novo synthesis of Drosha and Dicer in the embryo led to consistent developmental defects, a failure to gastrulate, and embryonic lethality, including changes in the steady state levels of transcription factors and signaling molecules involved in germ layer specification. We annotated and profiled small RNA expression from the ovary and several early embryonic stages by deep sequencing followed by computational analysis. miRNAs as well as a large population of putative piRNAs (piwi-interacting RNAs) had dynamic accumulation profiles through early development. Defects in morphogenesis caused by loss of Drosha could be rescued with four miRNAs. Taken together our results indicate that post-transcriptional gene regulation directed by miRNAs is functionally important for early embryogenesis and is an integral part of the early embryonic gene regulatory network in S. purpuratus., QC 20160128

Kertesz et al. (Nature Genetics 2008) described PITA, a miRNA target prediction algorithm based on hybridization energy and site accessibility. In this note, we used a population genomics approach to reexamine their data and found that the PITA algorithm had lower specificity than methods based on evolutionary conservation at comparable levels of sensitivity.We also showed that deeply conserved miRNAs tend to have stronger hybridization energies to their targets than do other miRNAs. Although PITA had higher specificity in predicting targets than a naïve seed-match method, this signal was primarily due to the use of a single cutoff score for all miRNAs and to the observed correlation between conservation and hybridization energy. Overall, our results clarify the accuracy of different miRNA target prediction algorithms in Drosophila and the role of site accessibility in miRNA target prediction., QC 20160118

Caenorhabditis elegans is one of the most prominent model systems for embryogenesis, but collecting many precisely staged embryos has been impractical. Thus, early C. elegans embryogenesis has not been amenable to most high-throughput genomics or biochemistry assays. To overcome this problem, we devised a method to collect staged C. elegans embryos by fluorescence-activated cell sorting (eFACS). In a proof-of-principle experiment, we found that a single eFACS run routinely yielded tens of thousands of almost perfectly staged 1-cell stage embryos. As the earliest embryonic events are driven by posttranscriptional regulation, we combined eFACS with second-generation sequencing to profile the embryonic expression of small, noncoding RNAs. We discovered complex and orchestrated changes in the expression between and within almost all classes of small RNAs, including microRNAs and 26G-RNAs, during embryogenesis., QC 20160127

The capacity of highly parallel sequencing technologies to detect small RNAs at unprecedented depth suggests their value in systematically identifying microRNAs (miRNAs). However, the identification of miRNAs from the large pool of sequenced transcripts from a single deep sequencing run remains a major challenge. Here, we present an algorithm, miRDeep, which uses a probabilistic model of miRNA biogenesis to score compatibility of the position and frequency of sequenced RNA with the secondary structure of the miRNA precursor. We demonstrate its accuracy and robustness using published Caenorhabditis elegans data and data we generated by deep sequencing human and dog RNAs. miRDeep reports altogether approximately 230 previously unannotated miRNAs, of which four novel C. elegans miRNAs are validated by northern blot analysis., QC 20151214