Tigerfish designs oligonucleotide-based in situ hybridization probes targeting intervals of highly repetitive DNA at the scale of genomes

Fluorescent in situ hybridization (FISH) is a powerful method for the targeted visualization of nucleic acids in their native contexts. Recent technological advances have leveraged computationally designed oligonucleotide (oligo) probes to interrogate > 100 distinct targets in the same sample, pushing the boundaries of FISH-based assays. However, even in the most highly multiplexed experiments, repetitive DNA regions are typically not included as targets, as the computational design of specific probes against such regions presents significant technical challenges. Consequently, many open questions remain about the organization and function of highly repetitive sequences. Here, we introduce Tigerfish, a software tool for the genome-scale design of oligo probes against repetitive DNA intervals. We showcase Tigerfish by designing a panel of 24 interval-specific repeat probes specific to each of the 24 human chromosomes and imaging this panel on metaphase spreads and in interphase nuclei. Tigerfish extends the powerful toolkit of oligo-based FISH to highly repetitive DNA.

High-capacity sample multiplexing for single cell chromatin accessibility profiling

Single-cell chromatin accessibility has emerged as a powerful means of understanding the epigenetic landscape of diverse tissues and cell types, but profiling cells from many independent specimens is challenging and costly. Here we describe a novel approach, sciPlex-ATAC-seq, which uses unmodified DNA oligos as sample-specific nuclear labels, enabling the concurrent profiling of chromatin accessibility within single nuclei from virtually unlimited specimens or experimental conditions. We first demonstrate our method with a chemical epigenomics screen, in which we identify drug-altered distal regulatory sites predictive of compound- and dose-dependent effects on transcription. We then analyze cell type-specific chromatin changes in PBMCs from multiple donors responding to synthetic and allogeneic immune stimulation. We quantify stimulation-altered immune cell compositions and isolate the unique effects of allogeneic stimulation on chromatin accessibility specific to T-lymphocytes. Finally, we observe that impaired global chromatin decondensation often coincides with chemical inhibition of allogeneic T-cell activation.

Nucleosome Patterns in Circulating Tumor DNA Reveal Transcriptional Regulation of Advanced Prostate Cancer Phenotypes

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and GATA2. To distinguish NEPC and ARPC in patient plasma samples, we developed prediction models that achieved accuracies of 97% for dominant phenotypes and 87% for mixed clinical phenotypes. Although phenotype classification is typically assessed by IHC or transcriptome profiling from tumor biopsies, we demonstrate that ctDNA provides comparable results with diagnostic advantages for precision oncology.

SIGNIFICANCE

This study provides insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. New methods for classification in phenotype mixtures extend the utility of ctDNA beyond assessments of somatic DNA alterations with important implications for molecular classification and precision oncology. This article is highlighted in the In This Issue feature, p. 517.

Modeling the impact of data sharing on variant classification

OBJECTIVE

Many genetic variants are classified, but many more are variants of uncertain significance (VUS). Clinical observations of patients and their families may provide sufficient evidence to classify VUS. Understanding how long it takes to accumulate sufficient patient data to classify VUS can inform decisions in data sharing, disease management, and functional assay development.

MATERIALS AND METHODS

Our software models the accumulation of clinical evidence (and excludes all other types of evidence) to measure their unique impact on variant interpretation. We illustrate the time and probability for VUS classification when laboratories share evidence, when they silo evidence, and when they share only variant interpretations.

RESULTS

Using conservative assumptions for frequencies of observed clinical evidence, our models show the probability of classifying rare pathogenic variants with an allele frequency of 1/100 000 increases from less than 25% with no data sharing to nearly 80% after one year when labs share data, with nearly 100% classification after 5 years. Conversely, our models found that extremely rare (1/1 000 000) variants have a low probability of classification using only clinical data.

DISCUSSION

These results quantify the utility of data sharing and demonstrate the importance of alternative lines of evidence for interpreting rare variants. Understanding variant classification circumstances and timelines provides valuable insight for data owners, patients, and service providers. While our modeling parameters are based on our own assumptions of the rate of accumulation of clinical observations, users may download the software and run simulations with updated parameters.

CONCLUSIONS

The modeling software is available at https://github.com/BRCAChallenge/classification-timelines.

Using species richness calculations to model the global profile of unsampled pathogenic variants: Examples from BRCA1 and BRCA2

There have been many surveys of genetic variation in BRCA1 and BRCA2 to identify variant prevalence and catalogue population specific variants, yet none have evaluated the magnitude of unobserved variation. We applied species richness estimation methods from ecology to estimate "variant richness" and determine how many germline pathogenic BRCA1/2 variants have yet to be identified and the frequency of these missing variants in different populations. We also estimated the prevalence of germline pathogenic BRCA1/2 variants and identified those expected to be most common. Data was obtained from a literature search including studies conducted globally that tested the entirety of BRCA1/2 for pathogenic variation. Across countries, 45% to 88% of variants were estimated to be missing, i.e., present in the population but not observed in study data. Estimated variant frequencies in each country showed a higher proportion of rare variants compared to recurrent variants. The median prevalence estimate of BRCA1/2 pathogenic variant carriers was 0.64%. BRCA1 c.68_69del is likely the most recurrent BRCA1/2 variant globally due to its estimated prevalence in India. Modeling variant richness using ecology methods may assist in evaluating clinical targeted assays by providing a picture of what is observed with estimates of what is still unknown.

Targeted long-read sequencing identifies missing pathogenic variants in unsolved Werner syndrome cases

BACKGROUND

Werner syndrome (WS) is an autosomal recessive progeroid syndrome caused by variants in WRN. The International Registry of Werner Syndrome has identified biallelic pathogenic variants in 179/188 cases of classical WS. In the remaining nine cases, only one heterozygous pathogenic variant has been identified.

METHODS

Targeted long-read sequencing (T-LRS) on an Oxford Nanopore platform was used to search for a second pathogenic variant in WRN. Previously, T-LRS was successfully used to identify missing variants and analyse complex rearrangements.

RESULTS

We identified a second pathogenic variant in eight of nine unsolved WS cases. In five cases, T-LRS identified intronic splice variants that were confirmed by either RT-PCR or exon trapping to affect splicing; in one case, T-LRS identified a 339 kbp deletion, and in two cases, pathogenic missense variants. Phasing of long reads predicted all newly identified variants were on a different haplotype than the previously known variant. Finally, in one case, RT-PCR previously identified skipping of exon 20; however, T-LRS did not detect a pathogenic DNA sequence variant.

CONCLUSION

T-LRS is an effective method for identifying missing pathogenic variants. Although limitations with computational prediction algorithms can hinder the interpretation of variants, T-LRS is particularly effective in identifying intronic variants.