SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.

MOTIVATION: Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. RESULTS: To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell-types that recapitulate expected organ structures. AVAILABILITY: SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection.

The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p = 1.158 × 10-10; Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.

Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data.

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 .

Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities.

Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and nonuniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial autocorrelation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell-cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomic data. We applied MERINGUE to a variety of spatially resolved transcriptomic data sets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomic data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease.

Assessing ZNF154 methylation in patient plasma as a multicancer marker in liquid biopsies from colon, liver, ovarian and pancreatic cancer patients.

One epigenetic hallmark of many cancer types is differential DNA methylation occurring at multiple loci compared to normal tissue. Detection and assessment of the methylation state at a specific locus could be an effective cancer diagnostic. We assessed the effectiveness of hypermethylation at the CpG island of ZNF154, a previously reported multi-cancer specific signature for use in a blood-based cancer detection assay. To predict its effectiveness, we compared methylation levels of 3698 primary tumors encompassing 11 solid cancers, 724 controls, 2711 peripheral blood cell samples, and 350 noncancer disease tissues from publicly available methylation array datasets. We performed a single-molecule high-resolution DNA melt analysis on 71 plasma samples from cancer patients and 20 noncancer individuals to assess ZNF154 methylation as a candidate diagnostic metric in liquid biopsy and compared results to KRAS mutation frequency in the case of pancreatic carcinoma. We documented ZNF154 hypermethylation in early stage tumors, which did not increase in most noncancer disease or with respect to age or sex in peripheral blood cells, suggesting it is a promising target in liquid biopsy. ZNF154 cfDNA methylation discriminated cases from healthy donor plasma samples in minimal plasma volumes and outperformed KRAS mutation frequency in pancreatic cancer.

Leveraging locus-specific epigenetic heterogeneity to improve the performance of blood-based DNA methylation biomarkers.

BACKGROUND: Variation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass ) that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification. RESULTS: We developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-ml) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers. CONCLUSIONS: Our results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitates the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically diverse ovarian carcinomas, indicating broad utility of ZNF154 for use as a biomarker of ovarian cancer.

Identification of human silencers by correlating cross-tissue epigenetic profiles and gene expression.

Compared to enhancers, silencers are notably difficult to identify and validate experimentally. In search for human silencers, we utilized H3K27me3-DNase I hypersensitive site (DHS) peaks with tissue specificity negatively correlated with the expression of nearby genes across 25 diverse cell lines. These regions are predicted to be silencers since they are physically linked, using Hi-C loops, or associated, using expression quantitative trait loci (eQTL) results, with a decrease in gene expression much more frequently than general H3K27me3-DHSs. Also, these regions are enriched for the binding sites of transcriptional repressors (such as CTCF, MECOM, SMAD4, and SNAI3) and depleted of the binding sites of transcriptional activators. Using sequence signatures of these regions, we constructed a computational model and predicted approximately 10,000 additional silencers per cell line and demonstrated that the majority of genes linked to these silencers are expressed at a decreased level. Furthermore, single nucleotide polymorphisms (SNPs) in predicted silencers are significantly associated with disease phenotypes. Finally, our results show that silencers commonly interact with enhancers to affect the transcriptional dynamics of tissue-specific genes and to facilitate fine-tuning of transcription in the human genome.

Transient reduction of DNA methylation at the onset of meiosis in male mice.

BACKGROUND: Meiosis is a specialized germ cell cycle that generates haploid gametes. In the initial stage of meiosis, meiotic prophase I (MPI), homologous chromosomes pair and recombine. Extensive changes in chromatin in MPI raise an important question concerning the contribution of epigenetic mechanisms such as DNA methylation to meiosis. Interestingly, previous studies concluded that in male mice, genome-wide DNA methylation patters are set in place prior to meiosis and remain constant subsequently. However, no prior studies examined DNA methylation during MPI in a systematic manner necessitating its further investigation. RESULTS: In this study, we used genome-wide bisulfite sequencing to determine DNA methylation of adult mouse spermatocytes at all MPI substages, spermatogonia and haploid sperm. This analysis uncovered transient reduction of DNA methylation (TRDM) of spermatocyte genomes. The genome-wide scope of TRDM, its onset in the meiotic S phase and presence of hemimethylated DNA in MPI are all consistent with a DNA replication-dependent DNA demethylation. Following DNA replication, spermatocytes regain DNA methylation gradually but unevenly, suggesting that key MPI events occur in the context of hemimethylated genome. TRDM also uncovers the prior deficit of DNA methylation of LINE-1 retrotransposons in spermatogonia resulting in their full demethylation during TRDM and likely contributing to the observed mRNA and protein expression of some LINE-1 elements in early MPI. CONCLUSIONS: Our results suggest that contrary to the prevailing view, chromosomes exhibit dynamic changes in DNA methylation in MPI. We propose that TRDM facilitates meiotic prophase processes and gamete quality control.

The Emergence of Pan-Cancer CIMP and Its Elusive Interpretation.

Epigenetic dysregulation is recognized as a hallmark of cancer. In the last 16 years, a CpG island methylator phenotype (CIMP) has been documented in tumors originating from different tissues. However, a looming question in the field is whether or not CIMP is a pan-cancer phenomenon or a tissue-specific event. Here, we give a synopsis of the history of CIMP and describe the pattern of DNA methylation that defines the CIMP phenotype in different cancer types. We highlight new conceptual approaches of classifying tumors based on CIMP in a cancer type-agnostic way that reveal the presence of distinct CIMP tumors in a multitude of The Cancer Genome Atlas (TCGA) datasets, suggesting that this phenotype may transcend tissue-type specificity. Lastly, we show evidence supporting the clinical relevance of CIMP-positive tumors and suggest that a common CIMP etiology may define new mechanistic targets in cancer treatment.

Repair of naturally occurring mismatches can induce mutations in flanking DNA.

'Normal' genomic DNA contains hundreds of mismatches that are generated daily by the spontaneous deamination of C (U/G) and methyl-C (T/G). Thus, a mutagenic effect of their repair could constitute a serious genetic burden. We show here that while mismatches introduced into human cells on an SV40-based episome were invariably repaired, this process induced mutations in flanking DNA at a significantly higher rate than no mismatch controls. Most mutations involved the C of TpC, the substrate of some single strand-specific APOBEC cytidine deaminases, similar to the mutations that can typify the 'mutator phenotype' of numerous tumors. siRNA knockdowns and chromatin immunoprecipitation showed that TpC preferring APOBECs mediate the mutagenesis, and siRNA knockdowns showed that both the base excision and mismatch repair pathways are involved. That naturally occurring mispairs can be converted to mutators, represents an heretofore unsuspected source of genetic changes that could underlie disease, aging, and evolutionary change.DOI: http://dx.doi.org/10.7554/eLife.02001.001.

Structure-based mutagenesis reveals critical residues in the transferrin receptor participating in the mechanism of pH-induced release of iron from human serum transferrin.

The recent crystal structure of two monoferric human serum transferrin (Fe(N)hTF) molecules bound to the soluble portion of the homodimeric transferrin receptor (sTFR) has provided new details about this binding interaction that dictates the delivery of iron to cells. Specifically, substantial rearrangements in the homodimer interface of the sTFR occur as a result of the binding of the two Fe(N)hTF molecules. Mutagenesis of selected residues in the sTFR highlighted in the structure was undertaken to evaluate the effect on function. Elimination of Ca(2+) binding in the sTFR by mutating two of four coordinating residues ([E465A,E468A]) results in low production of an unstable and aggregated sTFR. Mutagenesis of two histidines ([H475A,H684A]) at the dimer interface had little effect on the kinetics of release of iron at pH 5.6 from either lobe, reflecting the inaccessibility of this cluster to solvent. Creation of an H318A sTFR mutant allows assignment of a small pH-dependent initial decrease in the magnitude of the fluorescence signal to His318. Removal of the four C-terminal residues of the sTFR, Asp757-Asn758-Glu759-Phe760, eliminates pH-stimulated release of iron from the C-lobe of the Fe(2)hTF/sTFR Δ757-760 complex. The inability of this sTFR mutant to bind and stabilize protonated hTF His349 (a pH-inducible switch) in the C-lobe of hTF accounts for the loss. Collectively, these studies support a model in which a series of pH-induced events involving both TFR residue His318 and hTF residue His349 occurs to promote receptor-stimulated release of iron from the C-lobe of hTF.

Ionic residues of human serum transferrin affect binding to the transferrin receptor and iron release.

Efficient delivery of iron is critically dependent on the binding of diferric human serum transferrin (hTF) to its specific receptor (TFR) on the surface of actively dividing cells. Internalization of the complex into an endosome precedes iron removal. The return of hTF to the blood to continue the iron delivery cycle relies on the maintenance of the interaction between apohTF and the TFR after exposure to endosomal pH (≤6.0). Identification of the specific residues accounting for the pH-sensitive nanomolar affinity with which hTF binds to TFR throughout the cycle is important to fully understand the iron delivery process. Alanine substitution of 11 charged hTF residues identified by available structures and modeling studies allowed evaluation of the role of each in (1) binding of hTF to the TFR and (2) TFR-mediated iron release. Six hTF mutants (R50A, R352A, D356A, E357A, E367A, and K511A) competed poorly with biotinylated diferric hTF for binding to TFR. In particular, we show that Asp356 in the C-lobe of hTF is essential to the formation of a stable hTF-TFR complex: mutation of Asp356 in the monoferric C-lobe hTF background prevented the formation of the stoichiometric 2:2 (hTF:TFR monomer) complex. Moreover, mutation of three residues (Asp356, Glu367, and Lys511), whether in the diferric or monoferric C-lobe hTF, significantly affected iron release when in complex with the TFR. Thus, mutagenesis of charged hTF residues has allowed identification of a number of residues that are critical to formation of and release of iron from the hTF-TFR complex.