Genome-scale investigation of olfactory system spatial heterogeneity

An updated synthesis of and outstanding questions in the olfactory and vomeronasal systems in bats: Genetics asks questions only anatomy can answer

The extensive diversity observed in bat nasal chemosensory systems has been well-documented at the histological level. Understanding how this diversity evolved and developing hypotheses as to why particular patterns exist require a phylogenetic perspective, which was first outlined in the work of anatomist Kunwar Bhatnagar. With the onset of genetics and genomics, it might be assumed that the puzzling patterns observed in the morphological data have been clarified. However, there is still a widespread mismatch of genetic and morphological correlations among bat chemosensory systems. Novel genomic evidence has set up new avenues to explore that demand more evidence from anatomical structures. Here, we outline the progress that has been made in both morphological and molecular studies on the olfactory and vomeronasal systems in bats since the work of Bhatnagar. Genomic data of olfactory and vomeronasal receptors demonstrate the strong need for further morphological sampling, with a particular focus on receiving brain regions, glands, and ducts.

BEM: Mining Coregulation Patterns in Transcriptomics via Boolean Matrix Factorization

MOTIVATION

The matrix factorization is an important way to analyze coregulation patterns in transcriptomic data, which can reveal the tumor signal perturbation status and subtype classification. However, current matrix factorization methods do not provide clear bicluster structure. Furthermore, these algorithms are based on the assumption of linear combination, which may not be sufficient to capture the coregulation patterns.

RESULTS

We presented a new algorithm for Boolean matrix factorization (BMF) via expectation maximization (BEM). BEM is more aligned with the molecular mechanism of transcriptomic coregulation and can scale to matrix with over 100 million data points. Synthetic experiments showed that BEM outperformed other BMF methods in terms of reconstruction error. Real-world application demonstrated that BEM is applicable to all kinds of transcriptomic data, including bulk RNA-seq, single-cell RNA-seq and spatial transcriptomic datasets. Given appropriate binarization, BEM was able to extract coregulation patterns consistent with disease subtypes, cell types or spatial anatomy.

AVAILABILITY AND IMPLEMENTATION

Python source code of BEM is available on https://github.com/LifanLiang/EM_BMF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A deep learning based method for large-scale classification, registration, and clustering of in-situ hybridization experiments in the mouse olfactory bulb

BACKGROUND

The Allen Mouse Brain Atlas allows study of the brain's molecular anatomy at cellular scale, for thousands genes. To fully leverage this resource, one must register histological images of brain tissue - a task made challenging by the brain's structural complexity and heterogeneity, as well as inter-experiment variability.

NEW METHOD

We have developed a deep-learning based methodology for classification and registration of thousands of sections of brain tissue, using the mouse olfactory bulb (OB) as a case study.

RESULTS

We trained a convolutional neural network (CNN) to derive an image similarity measure for in-situ hybridization experiments, and embedded these in a low-dimensional feature space to guide the design of registration templates. We then compiled a high quality, registered atlas of gene expression for the OB (the first such atlas for the OB, to our knowledge). As proof-of-principle, the atlas was clustered using non-negative matrix factorization to reveal canonical expression motifs, and to identify novel, lamina-specific marker genes.

COMPARISON WITH EXISTING METHODS

Our method leverages virtues of CNNs for a set of important problems in molecular neuroanatomy, with performance comparable to existing methods.

CONCLUSION

The atlas we have complied allows for intra- and inter-laminar comparisons of gene expression patterns in the OB across thousands of genes, as well identification of canonical expression profiles through clustering. We anticipate that this will be a useful resource for investigators studying the bulb's development and functional topography. Our methods are publicly available for those interested in extending them to other brain areas.