The analysis of cell-free DNA (cfDNA) in plasma provides information on pathological processes in the body. Blood cfDNA is in the form of nucleosomes, which maintain their tissue- and cancer-specific epigenetic state. We developed a single-molecule multiparametric assay to comprehensively profile the epigenetics of plasma-isolated nucleosomes (EPINUC), DNA methylation and cancer-specific protein biomarkers. Our system allows for high-resolution detection of six active and repressive histone modifications and their ratios and combinatorial patterns on millions of individual nucleosomes by single-molecule imaging. In addition, our system provides sensitive and quantitative data on plasma proteins, including detection of non-secreted tumor-specific proteins, such as mutant p53. EPINUC analysis of a cohort of 63 colorectal cancer, 10 pancreatic cancer and 33 healthy plasma samples detected cancer with high accuracy and sensitivity, even at early stages. Finally, combining EPINUC with direct single-molecule DNA sequencing revealed the tissue of origin of colorectal, pancreatic, lung and breast tumors. EPINUC provides multilayered information of potential clinical relevance from limited (<1 ml) liquid biopsy material.

The CRISPR prime editor PE2 consists of a Streptococcus pyogenes Cas9 nickase (nSpCas9) fused at its C-terminus to a Moloney murine leukemia virus reverse transcriptase (MMLV-RT). Here we show that separated nSpCas9 and MMLV-RT proteins function as efficiently as intact PE2 in human cells. We use this Split-PE system to rapidly identify and engineer more compact prime editor architectures that also broaden the types of RTs used for prime editing.

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.

Here we describe a competitive genome editing method that measures the effect of mutations on molecular functions, based on precision CRISPR editing using template libraries with either the original or altered sequence, and a sequence tag, enabling direct comparison between original and mutated cells. Using the example of the MYC oncogene, we identify important transcriptional targets and show that E-box mutations at MYC target gene promoters reduce cellular fitness.

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.

Targeted degradation of cell surface and extracellular proteins via lysosomal delivery is an important means to modulate extracellular biology. However, these approaches have limitations due to lack of modularity, ease of development, restricted tissue targeting and applicability to both cell surface and extracellular proteins. We describe a lysosomal degradation strategy, termed cytokine receptor-targeting chimeras (KineTACs), that addresses these limitations. KineTACs are fully genetically encoded bispecific antibodies consisting of a cytokine arm, which binds its cognate cytokine receptor, and a target-binding arm for the protein of interest. We show that KineTACs containing the cytokine CXCL12 can use the decoy recycling receptor, CXCR7, to target a variety of target proteins to the lysosome for degradation. Additional KineTACs were designed to harness other CXCR7-targeting cytokines, CXCL11 and vMIPII, and the interleukin-2 (IL-2) receptor-targeting cytokine IL-2. Thus, KineTACs represent a general, modular, selective and simple genetically encoded strategy for inducing lysosomal delivery of extracellular and cell surface targets with broad or tissue-specific distribution.

Comment in Nat Methods. 2022 Nov;19(11):1338.

Here we introduce a mostly natural sequencing-by-synthesis (mnSBS) method for single-cell RNA sequencing (scRNA-seq), adapted to the Ultima genomics platform, and systematically benchmark it against current scRNA-seq technology. mnSBS uses mostly natural, unmodified nucleotides and only a low fraction of fluorescently labeled nucleotides, which allows for high polymerase processivity and lower costs. We demonstrate successful application in four scRNA-seq case studies of different technical and biological types, including 5' and 3' scRNA-seq, human peripheral blood mononuclear cells from a single individual and in multiplex, as well as Perturb-Seq. Benchmarking shows that results from mnSBS-based scRNA-seq are very similar to those using Illumina sequencing, with minor differences in results related to the position of reads relative to annotated gene boundaries, owing to single-end reads of Ultima being closer to gene ends than reads from Illumina. The method is thus compatible with state-of-the-art scRNA-seq libraries independent of the sequencing technology. We expect mnSBS to be of particular utility for cost-effective large-scale scRNA-seq projects.

Circular consensus sequencing with Pacific Biosciences (PacBio) technology generates long (10-25 kilobases), accurate 'HiFi' reads by combining serial observations of a DNA molecule into a consensus sequence. The standard approach to consensus generation, pbccs, uses a hidden Markov model. We introduce DeepConsensus, which uses an alignment-based loss to train a gap-aware transformer-encoder for sequence correction. Compared to pbccs, DeepConsensus reduces read errors by 42%. This increases the yield of PacBio HiFi reads at Q20 by 9%, at Q30 by 27% and at Q40 by 90%. With two SMRT Cells of HG003, reads from DeepConsensus improve hifiasm assembly contiguity (NG50 4.9 megabases (Mb) to 17.2 Mb), increase gene completeness (94% to 97%), reduce the false gene duplication rate (1.1% to 0.5%), improve assembly base accuracy (Q43 to Q45) and reduce variant-calling errors by 24%. DeepConsensus models could be trained to the general problem of analyzing the alignment of other types of sequences, such as unique molecular identifiers or genome assemblies.