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Medulloblastoma (MB) is the most common malignant childhood brain tumor1,2, yet the origin of the most aggressive subgroup-3 form remains elusive, impeding development of effective targeted treatments. Previous analyses of mouse cerebella3,4or human counterparts from frozen tissue nuclei5have not fully defined the compositional heterogeneity of MBs. Here, we undertook an unprecedented single-cell profiling of freshly-isolated human fetal cerebella to establish a reference map delineating hierarchical cellular states in MBs. We identified a unique transitional cerebellar progenitor connecting neural stem cells to neuronal lineages in developing fetal cerebella. Intersectional analysis revealed that the transitional progenitors were enriched in aggressive MB subgroups, including group-3 and metastatic tumors. Single-cell multi-omics revealed underlying regulatory networks in the transitional progenitor populations, including transcriptional determinants HNRNPH1 and SOX11, which are correlated with clinical prognosis in group-3 MBs. Genomic and Hi-C profiling identifiedde novolong-range chromatin-loops juxtaposing HNRNPH1/SOX11-targeted super-enhancers tocis-regulatory elements of MYC, an oncogenic driver for group-3 MBs. Targeting the transitional progenitor regulators inhibited MYC expression and MYC-driven group-3 MB growth. Our integrated single-cell atlases of human fetal cerebella and MBs reveal potential cell populations predisposed to transformation and regulatory circuitries underlying tumor cell states and oncogenesis, highlighting hitherto unrecognized transitional progenitor intermediates predictive of disease prognosis and potential therapeutic vulnerabilities.

Cis-regulatory elements coordinate the regulation of their targeted genes’ expression. However, the joint measurement of cis-regulatory elements’ activities and their interactions in spatial proximity is limited by the current sequencing approaches. We describe a method, NOMe-HiC, which simultaneously captures single nucleotide polymorphisms, DNA methylation, chromatin accessibility (GpC methyltransferase footprints), and chromosome conformation changes from the same DNA molecule, together with the transcriptome, in a single assay. NOMe-HiC shows high concordance with state-of-the-art mono-omic assays across different molecular measurements and reveals coordinated chromatin accessibility at distal genomic segments in spatial proximity and novel types of long-range allele-specific chromatin accessibility.

The fine-scale cell-free DNA fragmentation patterns in early-stage cancers are poorly understood. We developed a de novo approach to characterize the cell-free DNA fragmentation hotspots from plasma whole-genome sequencing. Hotspots are enriched in open chromatin regions, and, interestingly, 3′end of transposons. Hotspots showed global hypo-fragmentation in early-stage liver cancers and are associated with genes involved in the initiation of hepatocellular carcinoma and associated with cancer stem cells. The hotspots varied across multiple early-stage cancers and demonstrated high performance for the diagnosis and identification of tissue-of-origin in early-stage cancers. We further validated the performance with a small number of independent case–control-matched early-stage cancer samples.

SummaryCirculating cell-free DNA (cfDNA) is a promising biomarker for the diagnosis and prognosis of many diseases, including cancer. The genome-wide non-random fragmentation patterns of cfDNA are associated with the nucleosomal protection, epigenetic environment, and gene expression in the cell types that contributed to cfDNA. However, current progress on the development of computational methods and understanding of molecular mechanisms behind cfDNA fragmentation patterns is significantly limited by the controlled-access of cfDNA whole-genome sequencing (WGS) dataset. Here, we present FinaleDB (FragmentatIoN AnaLysis of cEll-free DNA DataBase), a comprehensive database to host thousands of uniformly processed and curated de-identified cfDNA WGS datasets across different pathological conditions. Furthermore, FinaleDB comes with a fragmentation genome browser, from which users can seamlessly integrate thousands of other omics data in different cell types to experience a comprehensive view of both gene-regulatory landscape and cfDNA fragmentation patterns.Availability and implementationFinaleDB service: http://finaledb.research.cchmc.org/ FinaleDB source code: https://github.com/epifluidlab/finaledb_portal and https://github.com/epifluidlab/finaledb_workflow.Contactlyping1986@gmail.com

The global variation of cell-free DNA fragmentation patterns is a promising biomarker for cancer diagnosis. However, the characterization of its hotspots and aberrations in early-stage cancer at the fine-scale is still poorly understood. Here, we developed an approach to de novo characterize genome-wide cell-free DNA fragmentation hotspots by integrating both fragment coverage and size from whole-genome sequencing. These hotspots are highly enriched in regulatory elements, such as promoters, and hematopoietic-specific enhancers. Surprisingly, half of the high-confident hotspots are still largely protected by the nucleosome and located near repeats, named inaccessible hotspots, which suggests the unknown origin of cell-free DNA fragmentation. In early-stage cancer, we observed the increases of fragmentation level at these inaccessible hotspots from microsatellite repeats and the decreases of fragmentation level at accessible hotspots near promoter regions, mostly with the silenced biological processes from peripheral immune cells and enriched in CTCF insulators. We identified the fragmentation hotspots from 298 cancer samples across 8 different cancer types (92% in stage I to III), 103 benign samples, and 247 healthy samples. The fine-scale fragmentation level at most variable hotspots showed cancer-specific fragmentation patterns across multiple cancer types and non-cancer controls. Therefore, with the fine-scale fragmentation signals alone in a machine learning model, we achieved 42% to 93% sensitivity at 100% specificity in different early-stage cancer. In cancer positive cases, we further localized cancer to a small number of anatomic sites with a median of 85% accuracy. The results here highlight the significance to characterize the fine-scale cell-free DNA fragmentation hotspot as a novel molecular marker for the screening of early-stage cancer that requires both high sensitivity and ultra-high specificity.

Cis-regulatory elements (CREs) play instrumental roles in the regulation of mammalian gene expression. Sets of transcriptional factors bring together multiple CREs, which can be up to megabases away, to initiate and maintain the gene expression. How to measure the activity of CREs and their coordination with the genetic variants over large genomic distances to regulate gene expression is the central question for regulatory genomics, which is still limited by the current sequencing approaches. We report a method, TAGMe-seq, which simultaneously captures the single nucleotide polymorphisms (SNPs), chromosome conformation, DNA methylation, and GpC methyltransferase footprints from a single molecule, together with transcriptome in the same assay.To demonstrate the performance of TAGMe-seq, we applied it to the IMR-90 and GM12878 cell lines, both of which have been comprehensively profiled publically. TAGMe-seq showed high concordances with the state-of-art mono-omics assays across all the molecular measurements at the same cell types. We next asked if the genomic regions linearly separated but positioned proximity in space would have coordinated footprint status. We analyzed the GCH (H=A, C, or T) methyltransferase footprint on TAGMe-seq reads separated in chromatin loop anchors. Indeed, the GCH methyltransferase footprint in TAGMe-seq read pairs mapping to separated loop anchors showed a significantly higher correlation than that in the shuffled read pairs from the same genomic regions. To further demonstrate the utility of TAGMe-seq, we utilized it to analyze the long-range (>1kb) allele-specific GpC methyltransferase footprint. We separated TAGMe-seq reads into two groups by their parent-of-origin at the heterozygous SNPs (SNP anchors). Naturally, the other ends of the read pairs were also separated into two alleles, even though they are not overlapped with the SNPs and mapped over large genomic distances (non-SNP anchors). Thus, we are able to analyze the long-range allele-specific GCH methyltransferase footprint. We identified three groups of allele-specific footprint loci based on their differences of GCH accessibility between the two alleles at both anchors, or only at SNP anchors, or only at non-SNP anchors, which showed distinct enrichment levels of TF binding. Overall, combining multiple assays in TAGMe-seq allowed us to uncover the coordinated GpC methyltransferase footprint and long-range allele-specific footprint at genomic regions that are far away in linear DNA sequences but spatially close in the nucleus, which will eventually pave the road to dissect the mechanisms of gene-regulatory aberrations in complex diseases, such as cancer. Citation Format: Hailu Fu, Haizi Zheng, Louis J. Muglia, Li Wang, Yaping Liu. Simultaneous profiling of genetic variants, 3D genome, GpC methyltransferase footprints, DNA methylation, and transcriptome in a single assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5797.

To evaluate any possible DNA contamination in Small Input Liquid Volume Extracellular RNA Sequencing (SILVER-seq), we carried out three types of tests. First, we tested the effectiveness of SILVER-seq’s DNase treatment step. Without DNase treatment, five of the five tested serum samples exhibited detectable cell-free DNA (cfDNA; Fig. 1 A ). After SILVER-seq’s DNase treatment step, none of the serum samples exhibited any detectable cfDNA (Fig. 1 A ), suggesting that SILVER-seq’s DNase treatment step thoroughly digested cfDNA in human serum samples. Fig. 1. DNA contamination tests. ( A ) Concentrations of cfDNA in five human serum samples (IDs: hS01 to hS05, columns) before and after SILVER-seq’s DNase treatment step. Each serum sample (column) was split into two 7-µL aliquots which were subjected to no treatment ( Upper ) or DNase treatment ( Lower ). ND, not detectable. The detection limit of Qubit’s high-sensitivity kit is 0.5 pg/µL. ( B ) Measured concentrations of cfDNA titers (columns) before ( Upper ) and after ( Lower ) SILVER-seq’s DNase treatment step. The detection limit of … [↵][1]2To whom correspondence may be addressed. Email: shuchien{at}ucsd.edu, hisu{at}ucsd.edu, or szhong{at}ucsd.edu. [1]: #xref-corresp-1-1

Significance The SILVER-seq technology enables sequencing extracellular RNAs (exRNAs) from a single droplet of liquid biopsy. This study revealed strong associations between serum exRNA expression levels and the donor’s sex and age. SILVER-seq detected serum exRNAs from the genes that are only expressed in brain, suggesting the possibility of monitoring brain gene expression from a blood test. Classifiers based on exRNA expression levels were able to separate breast cancer patients from control donors. The exRNA-based classifiers could also distinguish the patients with recurrent cancer from other breast cancer patients. The SILVER-seq technology can therefore lead the way to future in vitro diagnostics trials based on finger prick blood, which is more accessible for screening and frequent monitoring of human diseases., Extracellular RNAs (exRNAs) are present in human serum. It remains unclear to what extent these circulating exRNAs may reflect human physiologic and disease states. Here, we developed SILVER-seq (Small Input Liquid Volume Extracellular RNA Sequencing) to efficiently sequence both integral and fragmented exRNAs from a small droplet (5 μL to 7 μL) of liquid biopsy. We calibrated SILVER-seq in reference to other RNA sequencing methods based on milliliters of input serum and quantified droplet-to-droplet and donor-to-donor variations. We carried out SILVER-seq on more than 150 serum droplets from male and female donors ranging from 18 y to 48 y of age. SILVER-seq detected exRNAs from more than a quarter of the human genes, including small RNAs and fragments of mRNAs and long noncoding RNAs (lncRNAs). The detected exRNAs included those derived from genes with tissue (e.g., brain)-specific expression. The exRNA expression levels separated the male and female samples and were correlated with chronological age. Noncancer and breast cancer donors exhibited pronounced differences, whereas donors with or without cancer recurrence exhibited moderate differences in exRNA expression patterns. Even without using differentially expressed exRNAs as features, nearly all cancer and noncancer samples and a large portion of the recurrence and nonrecurrence samples could be correctly classified by exRNA expression values. These data suggest the potential of using exRNAs in a single droplet of serum for liquid biopsy-based diagnostics.

We thank Drs. Hartl and Gao for pointing out the difference in read lengths between the cancer and normal samples (1). Indeed, Illumina discontinued their 50-base pair (bp) sequencing kits after we finished sequencing the cancer samples, forcing us to switch to 75-bp sequencing kits on the normal samples. The different sequencing kits did incur small changes of sequence alignment metrics. While acknowledging the possibility that this may bias the assessment of cancer vs. normal discrimination performance of our method, we believe such a possibility is substantially ameliorated by the following considerations. First, a recent study reported that “with the exception of 25 bp reads, there is little difference for the detection of … [↵][1]1To whom correspondence may be addressed. Email: shuchien{at}ucsd.edu, hisu{at}ucsd.edu, or szhong{at}ucsd.edu. [1]: #xref-corresp-1-1