Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve . <<<

Sachet A Shukla , Michael S Rooney , Mohini Rajasagi , Grace Tiao , Philip M Dixon , Michael S Lawrence , Jonathan Stevens , William J Lane , Jamie L Dellagatta , Scott Steelman , Carrie Sougnez , Kristian Cibulskis , Adam Kiezun , Nir Hacohen , Vladimir Brusic , Catherine J Wu , Gad Getz <<<

Detection of somatic mutations in human leukocyte antigen (HLA) genes using whole-exome sequencing (WES) is hampered by the high polymorphism of the HLA loci, which prevents alignment of sequencing reads to the human reference genome. We describe a computational pipeline that enables accurate inference of germline alleles of class I HLA-A, B and C genes and subsequent detection of mutations in these genes using the inferred alleles as a reference. Analysis of WES data from 7,930 pairs of tumor and healthy tissue from the same patient revealed 298 nonsilent HLA mutations in tumors from 266 patients. These 298 mutations are enriched for likely functional mutations, including putative loss-of-function events. Recurrence of mutations suggested that these 'hotspot' sites were positively selected. Cancers with recurrent somatic HLA mutations were associated with upregulation of signatures of cytolytic activity characteristic of tumor infiltration by effector lymphocytes, supporting immune evasion by altered HLA function as a contributory mechanism in cancer. <<<

We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy. <<<

BACKGROUND: Analysis of somatic mutations provides insight into the mutational processes that have shaped the cancer genome, but such analysis currently requires large cohorts. We develop deconstructSigs, which allows the identification of mutational signatures within a single tumor sample.RESULTS: Application of deconstructSigs identifies samples with DNA repair deficiencies and reveals distinct and dynamic mutational processes molding the cancer genome in esophageal adenocarcinoma compared to squamous cell carcinomas.CONCLUSIONS: deconstructSigs confers the ability to define mutational processes driven by environmental exposures, DNA repair abnormalities, and mutagenic processes in individual tumors with implications for precision cancer medicine. <<<

BACKGROUND: The clinical utility of comprehensive genomic profiling (CGP) for guiding treatment has gradually become the standard-of-care procedure for colorectal carcinoma (CRC). Here, we comprehensively assess emerging targeted therapy biomarkers using CGP in primary CRC.METHODS: A total of 575 primary CRCs were sequenced by ACTOnco® assay for genomic alterations, tumour mutational burden (TMB), and microsatellite instability (MSI).RESULTS: Eighteen percent of patients were detected as MSI-High (MSI-H), and the remaining cases were classified as microsatellite stable (MSS). Driver mutation prevalence in MSS CRCs were APC (74%), TP53 (67%), KRAS (47%), PIK3CA (21%) and BRAF (13%). The median TMBs for MSI-H and MSS patients were 37.8 mutations per mega base (mut/Mb) and 3.9 mut/Mb, respectively. Forty-seven percent of MSI-H CRC harboured at least one loss-of-function mutations in genes that may hamper immune checkpoint blockade. Among MSS RAS/RAF wild-type CRCs, 59% had at least one actionable mutation that may compromise the efficacy of anti-EGFR therapy. For late-stage CRC, 51% of patients are eligible for standard care actionability and the remaining 49% could be enrolled in clinical trials with investigational drugs.CONCLUSIONS: This study highlights the essential role of CGP for identifying rational targeted therapy options in CRC. <<<

Alexander J M Dingemans, Kim M G Truijen, Sam van de Ven, Raphael Bernier, Ernie M H F Bongers, Arjan Bouman, Laura de Graaff-Herder, Evan E Eichler, Erica H Gerkes, Christa M De Geus, Johanna M van Hagen, Philip R Jansen, Jennifer Kerkhof, Anneke J A Kievit, Tjitske Kleefstra, Saskia M Maas, Stella A de Man, Haley McConkey, Wesley G Patterson, Amy T Dobson, Eloise J Prijoles, Bekim Sadikovic, Raissa Relator, Roger E Stevenson, Connie T R M Stumpel, Malou Heijligers, Kyra E Stuurman, Katharina Löhner, Shimriet Zeidler, Jennifer A Lee, Amanda Lindy, Fanggeng Zou, Matthew L Tedder, Lisenka E L M Vissers, Bert B A de Vries <<<

CHD8, a major autism gene, functions in chromatin remodelling and has various roles involving several biological pathways. Therefore, unsurprisingly, previous studies have shown that intellectual developmental disorder with autism and macrocephaly (IDDAM), the syndrome caused by pathogenic variants in CHD8, consists of a broad range of phenotypic abnormalities. We collected and reviewed 106 individuals with IDDAM, including 36 individuals not previously published, thus enabling thorough genotype-phenotype analyses, involving the CHD8 mutation spectrum, characterization of the CHD8 DNA methylation episignature, and the systematic analysis of phenotypes collected in Human Phenotype Ontology (HPO). We identified 29 unique nonsense, 25 frameshift, 24 missense, and 12 splice site variants. Furthermore, two unique inframe deletions, one larger deletion (exons 26-28), and one translocation were observed. Methylation analysis was performed for 13 patients, 11 of which showed the previously established episignature for IDDAM (85%) associated with CHD8 haploinsufficiency, one analysis was inconclusive, and one showing a possible gain-of-function signature instead of the expected haploinsufficiency signature was observed. Consistent with previous studies, phenotypical abnormalities affected multiple organ systems. Many neurological abnormalities, like intellectual disability (68%) and hypotonia (29%) were observed, as well as a wide variety of behavioural abnormalities (88%). Most frequently observed behavioural problems included autism spectrum disorder (76%), short attention span (32%), abnormal social behaviour (31%), sleep disturbance (29%) and impaired social interactions (28%). Furthermore, abnormalities in the digestive (53%), musculoskeletal (79%) and genitourinary systems (18%) were noted. Although no significant difference in severity was observed between males and females, individuals with a missense variant were less severely affected. Our study provides an extensive review of all phenotypic abnormalities in patients with IDDAM and provides clinical recommendations, which will be of significant value to individuals with a pathogenic variant in CHD8, their families, and clinicians as it gives a more refined insight into the clinical and molecular spectrum of IDDAM, which is essential for accurate care and counselling. <<<

Recent advances in spatially resolved transcriptomics (SRT) have revolutionized biological and medical research and enabled unprecedented insight into the functional organization and cell communication of tissues and organs i. Identifying and elucidating gene spatial expression variation (SE analysis) is fundamental to elucidate the SRT landscape. There is an urgent need for public repositories and computational techniques of SRT data in SE analysis alongside technological breakthroughs and large-scale data generation. Increasing efforts to use techniques in SE analysis have been made. However, these attempts are widely scattered among a large number of studies that are not easily accessible or comprehensible by both medical and life scientists. This study provides a survey and a summary of public resources on SE analysis in SRT studies. An updated systematic overview of state-of-the-art computational approaches and tools currently available in SE analysis are presented herein, emphasizing recent advances. Finally, the present study explores the future perspectives and challenges of techniques in SE analysis. This study guides medical and life scientists to look for dedicated resources and more competent tools for characterizing spatial patterns of gene expression. <<<

BACKGROUND: Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics.RESULTS: First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics.CONCLUSIONS: The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving. <<<

Gioele La Manno, Ruslan Soldatov, Amit Zeisel, Emelie Braun, Hannah Hochgerner, Viktor Petukhov, Katja Lidschreiber, Maria E Kastriti, Peter Lönnerberg, Alessandro Furlan, Jean Fan, Lars E Borm, Zehua Liu, David van Bruggen, Jimin Guo, Xiaoling He, Roger Barker, Erik Sundström, Gonçalo Castelo-Branco, Patrick Cramer, Igor Adameyko, Sten Linnarsson, Peter V Kharchenko <<<

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity-the time derivative of the gene expression state-can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans. <<<

BACKGROUND: Lymphocytic leukemia (LL) is a primary malignant tumor of hematopoietic tissue, which seriously affects the health of children and the elderly. The study aims to establish a new detection method for screening acute/chronic LL using time-resolved fluorescence immunoassay (TRFIA) via quantitative detection of S100 calcium binding protein A8 (S100A8) and leucine-rich alpha-2-glycoprotein 1 (LRG1) in serum.METHODS: Here a sandwich TRFIA was optimized and established: Anti-S100A8/LRG1 caputre antibodies immobilized on 96-well plates captured S100A8/LRG1, and then banded together with the anti-S100A8/LRG1 detection antibodies labeled with Europium(III) (Eu3+)/samarium(III) (Sm3+) chelates. Finally time resolved fluorometry measured the fluorescence intensity.RESULTS: The sensitivity of S100A8 was 1.15 ng/mL(LogY = 3.4027 + 0.4091 × LogX, R2 = 0.9828, P < 0.001, dynamic range: 2.1-10,000 ng/mL), and 3.2 ng/mL for LRG1 (LogY = 3.3009 + 0.4082 × LogX, R2 = 0.9748, P < 0.001, dynamic range: 4.0-10,000 ng/mL). The intra-assay and inter-assay CVs were low, ranging from 5.75% to 8.23% for S100A8 and 5.30% to 9.45% for LRG1 with high specificity and affinity in serum samples. Bland-Altman plots indicated TRFIA and ELISA kits have good agreement in clinical serum samples. Additionally, the cutoff values for S100A8 and LRG1 were 1849.18 ng/mL and 588.08 ng/mL, respectively.CONCLUSION: The present TRFIA method could be used for the quantitative detection of S100A8 and LRG1 in serum, and it has high sensitivity, accuracy and specificity. Clinically, this TRFIA method could be suitable for screening of LL via the quantitative detection of S100A8 and LRG1. <<<

Here, we present 12 loci paternal haplotypes (Y-STR profiles) against the backdrop of the Y-SNP marker system of Bantu males from the Maputo Province of Southeast Africa, a region believed to represent the southeastern fringe of the Bantu expansion. Our Maputo Bantu group was analyzed within the context of 27 geographically relevant reference populations in order to ascertain its genetic relationship to other Bantu and non Bantu (Pygmy, Khoisan and Nilotic) sub-equatorial tribes from West and East Africa. This study entails statistical pair wise comparisons and multidimensional scaling based on YSTR Rst distances, network analyses of Bantu (B2a-M150) and Pygmy (B2b-M112) lineages as well as an assessment of Y-SNP distribution patterns. Several notable findings include the following: 1) the Maputo Province Bantu exhibits a relatively close paternal affinity with both east and west Bantu tribes due to high proportion of Bantu Y chromosomal markers, 2) only traces of Khoisan (1.3%) and Pygmy (1.3%) markers persist in the Maputo Province Bantu gene pool, 3) the occurrence of R1a1a-M17/M198, a member of the Eurasian R1a-M420 branch in the population of the Maputo Province, may represent back migration events and/or recent admixture events, 4) the shared presence of E1b1b1-M35 in all Tanzanian tribes examined, including Bantu and non-Bantu groups, in conjunction with its nearly complete absence in the West African populations indicate that, in addition to a shared linguistic, cultural and genetic heritage, geography (e.g., east vs. west) may have impacted the paternal landscape of sub-Saharan Africa, 5) the admixture and assimilation processes of Bantu elements were both highly complex and region-specific. <<<

Lian Narunsky-Haziza, Gregory D Sepich-Poore, Ilana Livyatan, Omer Asraf, Cameron Martino, Deborah Nejman, Nancy Gavert, Jason E Stajich, Guy Amit, Antonio González, Stephen Wandro, Gili Perry, Ruthie Ariel, Arnon Meltser, Justin P Shaffer, Qiyun Zhu, Nora Balint-Lahat, Iris Barshack, Maya Dadiani, Einav N Gal-Yam, Sandip Pravin Patel, Amir Bashan, Austin D Swafford, Yitzhak Pilpel, Rob Knight, Ravid Straussman <<<

Cancer-microbe associations have been explored for centuries, but cancer-associated fungi have rarely been examined. Here, we comprehensively characterize the cancer mycobiome within 17,401 patient tissue, blood, and plasma samples across 35 cancer types in four independent cohorts. We report fungal DNA and cells at low abundances across many major human cancers, with differences in community compositions that differ among cancer types, even when accounting for technical background. Fungal histological staining of tissue microarrays supported intratumoral presence and frequent spatial association with cancer cells and macrophages. Comparing intratumoral fungal communities with matched bacteriomes and immunomes revealed co-occurring bi-domain ecologies, often with permissive, rather than competitive, microenvironments and distinct immune responses. Clinically focused assessments suggested prognostic and diagnostic capacities of the tissue and plasma mycobiomes, even in stage I cancers, and synergistic predictive performance with bacteriomes. <<<

The advent of immune checkpoint inhibition (ICI) using antibodies against PD1 and its ligand PDL1 has prompted substantial efforts to develop complementary drugs. Although many of these are antibodies directed against additional checkpoint proteins, there is an increasing interest in small-molecule immuno-oncology drugs that address intracellular pathways, some of which have recently entered clinical trials. In parallel, small molecules that target pro-tumorigenic pathways in cancer cells and the tumour microenvironment have been found to have immunostimulatory effects that synergize with the action of ICI antibodies, leading to the approval of an increasing number of regimens that combine such drugs. Combinations with small molecules targeting cancer metabolism, cytokine/chemokine and innate immune pathways, and T cell checkpoints are now under investigation. This Review discusses the recent milestones and hurdles encountered in this area of drug development, as well as our views on the best path forward. <<<

G(M1) gangliosidosis and Morquio B are autosomal recessive lysosomal storage diseases associated with a neurodegenerative disorder or dwarfism and skeletal abnormalities, respectively. These diseases are caused by deficiencies in the lysosomal enzyme β-d-galactosidase (β-Gal), which lead to accumulations of the β-Gal substrates, G(M1) ganglioside, and keratan sulfate. β-Gal is an exoglycosidase that catalyzes the hydrolysis of terminal β-linked galactose residues. This study shows the crystal structures of human β-Gal in complex with its catalytic product galactose or with its inhibitor 1-deoxygalactonojirimycin. Human β-Gal is composed of a catalytic TIM barrel domain followed by β-domain 1 and β-domain 2. To gain structural insight into the molecular defects of β-Gal in the above diseases, the disease-causing mutations were mapped onto the three-dimensional structure. Finally, the possible causes of the diseases are discussed. <<<

Maternal brain adaptations have been found across pregnancy and postpartum, but little is known about the long-term effects of parity on the maternal brain. Using neuroimaging and machine learning, we investigated structural brain characteristics in 12,021 middle-aged women from the UK Biobank, demonstrating that parous women showed less evidence of brain aging compared to their nulliparous peers. The relationship between childbirths and a "younger-looking" brain could not be explained by common genetic variation or relevant confounders. Although prospective longitudinal studies are needed, the results suggest that parity may involve neural changes that could influence women's brain aging later in life. <<<

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combined to generate target segmentation via label fusion schemes. Many conventional MAS methods employed the atlases from the same modality as the target image. However, the number of atlases with the same modality may be limited or even missing in many clinical applications. Besides, conventional MAS methods suffer from the computational burden of registration or label fusion procedures. In this work, we design a novel cross-modality MAS framework, which uses available atlases from a certain modality to segment a target image from another modality. To boost the computational efficiency of the framework, both the image registration and label fusion are achieved by well-designed deep neural networks. For the atlas-to-target image registration, we propose a bi-directional registration network (BiRegNet), which can efficiently align images from different modalities. For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image. SimNet can learn multi-scale information for similarity estimation to improve the performance of label fusion. The proposed framework was evaluated by the left ventricle and liver segmentation tasks on the MM-WHS and CHAOS datasets, respectively. Results have shown that the framework is effective for cross-modality MAS in both registration and label fusion. <<<

Atlas building and image registration are important tasks for medical image analysis. Once one or multiple atlases from an image population have been constructed, commonly (1) images are warped into an atlas space to study intra-subject or inter-subject variations or (2) a possibly probabilistic atlas is warped into image space to assign anatomical labels. Atlas estimation and nonparametric transformations are computationally expensive as they usually require numerical optimization. Additionally, previous approaches for atlas building often define similarity measures between a fuzzy atlas and each individual image, which may cause alignment difficulties because a fuzzy atlas does not exhibit clear anatomical structures in contrast to the individual images. This work explores using a convolutional neural network (CNN) to jointly predict the atlas and a stationary velocity field (SVF) parameterization for diffeomorphic image registration with respect to the atlas. Our approach does not require affine pre-registrations and utilizes pairwise image alignment losses to increase registration accuracy. We evaluate our model on 3D knee magnetic resonance images (MRI) from the OAI-ZIB dataset. Our results show that the proposed framework achieves better performance than other state-of-the-art image registration algorithms, allows for end-to-end training, and for fast inference at test time. <<<

In a search for the genetic basis for the structural difference between the related Streptococcus pneumoniae capsular serotypes 15B and 15C and for the reported reversible switching between these serotypes, the corresponding capsular polysaccharide synthesis (cps) loci were investigated by keeping in mind that at the structural level, the capsules differ only in O acetylation. The cps locus of a serotype 15B strain was identified, partially PCR amplified with primers based on the related serotype 14 sequence, and sequenced. Sequence analysis revealed, among other open reading frames, an intact open reading frame (designated cps15bM) whose product, at the protein level, exhibited characteristics of previously identified acetyltransferases. Genetic analysis of the corresponding region in a serotype15C strain indicated that the same gene was present but had a premature stop in translation. Closer analysis indicated that the serotype 15B gene contained a short tandem TA repeat consisting of eight TA units. In serotype 15C, this gene contained nine TA units that resulted in a frameshift and a truncated product. Genetic analysis of 17 serotype 15B and 15C clinical isolates revealed a perfect correlation between the serotype and the length of the short tandem repeat in the putative O-acetyltransferase gene. The number of TA repeating units varied between seven and nine in the various isolates. Together, the data strongly suggest that the structural difference between serotypes 15B and 15C is based on variation in the short tandem TA repeat in the O-acetyltransferase gene and that the transition between serotypes is due to slipped-strand mispairing with deletion or insertion of TA units in the cps15bM gene. <<<

Diffusion models are a class of deep generative models that have shown impressive results on various tasks with a solid theoretical foundation. Despite demonstrated success than state-of-the-art approaches, diffusion models often entail costly sampling procedures and sub-optimal likelihood estimation. Significant efforts have been made to improve the performance of diffusion models in various aspects. In this article, we present a comprehensive review of existing variants of diffusion models. Specifically, we provide the taxonomy of diffusion models and categorize them into three types: sampling-acceleration enhancement, likelihood-maximization enhancement, and data-generalization enhancement. We also introduce the other generative models (i.e., variational autoencoders, generative adversarial networks, normalizing flow, autoregressive models, and energy-based models) and discuss the connections between diffusion models and these generative models. Then we review the applications of diffusion models, including computer vision, natural language processing, waveform signal processing, multi-modal modeling, molecular graph generation, time series modeling, and adversarial purification. Furthermore, we propose new perspectives pertaining to the development of generative models. Github: this https URL. <<<

The ability to select short DNA oligonucleotide sequences capable of binding solely to their intended target is of great importance in developing nucleic acid based detection technologies. Applications such as multiplex PCR rely on primers binding to unique regions in a genome. Competing side reactions with other primer pairs or template DNA decrease PCR efficiency. Freely available primer design software such as Primer3 screens for potential hairpin and primer-dimer interactions while selecting a single primer pair. The development of multiplex PCR assays (in the range of 5 to 20 loci) requires the screening of all primer pairs for potential cross-reactivity. However, a logistical problem results due to the number of total number of comparisons required. Comparing the primer set for a 10-plex assay (20 total primer sequences) results in 210 primer-primer combinations that must be screened. The ability to screen sets of candidate oligomers rapidly for potential cross-reactivity reduces overall assay development time. Here we report the application of a familiar sliding algorithm for comparing two strands of DNA in an overlapping fashion. The algorithm has been employed in a software package wherein the user can compare multiple sequences in a single computational run. After the screening is completed, a score is assigned to potential duplex interactions exceeding a user-defined threshold. Additional criteria of predicted melting temperature (Tm) and free energy of melting (ΔG) are included for further ranking. Sodium counterion and total stand concentrations can be adjusted for the Tm and ΔG calculations. The predicted interactions are saved in a text file for further evaluation. <<<