A Rare Subset of Primary Tumor Cells with Concomitant Hyperactivation of Extracellular Matrix Remodeling and dsRNA-IFN1 Signaling Metastasizes in Breast Cancer.

Metastatic breast cancer has a poor prognosis and is largely considered incurable. A better understanding of the molecular determinants of breast cancer metastasis could facilitate development of improved prevention and treatment strategies. We used lentiviral barcoding coupled to single-cell RNA sequencing to trace clonal and transcriptional evolution during breast cancer metastasis and showed that metastases derive from rare prometastatic clones that are underrepresented in primary tumors. Both low clonal fitness and high metastatic potential were independent of clonal origin. Differential expression and classification analyses revealed that the prometastatic phenotype was acquired by rare cells characterized by the concomitant hyperactivation of extracellular matrix remodeling and dsRNA-IFN signaling pathways. Notably, genetic silencing of key genes in these pathways (KCNQ1OT1 or IFI6, respectively) significantly impaired migration in vitro and metastasis in vivo, with marginal effects on cell proliferation and tumor growth. Gene expression signatures derived from the identified prometastatic genes predict metastatic progression in patients with breast cancer, independently of known prognostic factors. This study elucidates previously unknown mechanisms of breast cancer metastasis and provides prognostic predictors and therapeutic targets for metastasis prevention. SIGNIFICANCE: Transcriptional lineage tracing coupled with single-cell transcriptomics defined the transcriptional programs underlying metastatic progression in breast cancer, identifying prognostic signatures and prevention strategies.

Barcode demultiplexing of nanopore sequencing raw signals by unsupervised machine learning.

Introduction: Oxford Nanopore Technologies (ONT) is a third generation sequencing approach that allows the analysis of individual, full-length nucleic acids. ONT records the alterations of an ionic current flowing across a nano-scaled pore while a DNA or RNA strand is threading through the pore. Basecalling methods are then leveraged to translate the recorded signal back to the nucleic acid sequence. However, basecall generally introduces errors that hinder the process of barcode demultiplexing, a pivotal task in single-cell RNA sequencing that allows for separating the sequenced transcripts on the basis of their cell of origin. Methods: To solve this issue, we present a novel framework, called UNPLEX, designed to tackle the barcode demultiplexing problem by operating directly on the recorded signals. UNPLEX combines two unsupervised machine learning methods: autoencoders and self-organizing maps (SOM). The autoencoders extract compact, latent representations of the recorded signals that are then clustered by the SOM. Results and Discussion: Our results, obtained on two datasets composed of in silico generated ONT-like signals, show that UNPLEX represents a promising starting point for the development of effective tools to cluster the signals corresponding to the same cell.

Topological Data Analysis for Revealing the Structural Origin of Density Anomalies in Silica Glass.

Topological data analysis (TDA) is a newly emerging and powerful tool for understanding the medium-range structure ordering of multiscale data. This study investigates the density anomalies observed during the cooling of liquid silica from a topological point of view using TDA. The density of liquid silica does not monotonically increase during cooling; it instead shows a maximum and minimum. Despite tremendous efforts, the structural origin of these density anomalies is not clearly understood. Our approach reveals that the one-dimensional topology of the -Si-Si- network changes at the temperatures at which the maximum and minimum densities are observed in our MD simulations, while those of the -O-O- and -Si-O- networks change at lower temperatures. Our ring analysis motivated by the TDA outcomes reveals that quantitative changes in -Si-Si- rings occur at the temperatures where the density is maximized and minimized, while those of the -O-O- and -Si-O- rings occur at lower temperatures; such findings are perfectly consistent with our TDA results. Our work demonstrates the value of new topological techniques in understanding the transitions in glassy materials and sheds light on the characterization of glass-liquid transitions.

Shry: Application of Canonical Augmentation to the Atomic Substitution Problem.

A common approach for studying a solid solution or disordered system within a periodic ab initio framework is to create a supercell in which certain amounts of target elements are substituted with other elements. The key to generating supercells is determining how to eliminate symmetry-equivalent structures from many substitution patterns. Although the total number of substitutions is on the order of trillions, only symmetry-inequivalent atomic substitution patterns need to be identified, and their number is far smaller than the total. Our developed Python software package, which is called Shry (Suite for High-throughput generation of models with atomic substitutions implemented by Python), allows the selection of only symmetry-inequivalent structures from the vast number of candidates based on the canonical augmentation algorithm. Shry is implemented in Python 3 and uses the CIF format as the standard for both reading and writing the reference and generated sets of substituted structures. Shry can be integrated into another Python program as a module or can be used as a stand-alone program. The implementation was verified through a comparison with other codes with the same functionality, based on the total numbers of symmetry-inequivalent structures, and also on the equivalencies of the output structures themselves. The provided crystal structure data used for the verification are expected to be useful for benchmarking other codes and also developing new algorithms in the future.