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The design and implementation of single-cell experiments is often limited by their requirement for fresh starting material. We have adapted a method for histological tissue fixation using dithio-bis(succinimidyl propionate) (DSP), or Lomant’s Reagent, to stabilise cell samples for single-cell transcriptomic applications. DSP is a reversible cross-linker of free amine groups that has previously been shown to preserve tissue integrity for histology while maintaining RNA integrity and yield in bulk RNA extractions. Although RNA-seq data from DSP-fixed single cells appears to be prone to characteristic artefacts, such as slightly reduced yield of cDNA and a detectable 3′ bias in comparison with fresh cells, cell preservation using DSP does not appear to substantially reduce RNA complexity at the gene level. In addition, there is evidence that instantaneous fixation of cells can reduce inter-cell technical variability. The ability of DSP-fixed cells to retain commonly used dyes, such as propidium iodide, enables the tracking of experimental sub-populations and the recording of cell viability at the point of fixation. Preserving cells using DSP will remove several barriers in the staging of single-cell experiments, including the transport of samples and the scheduling of shared equipment for downstream single-cell isolation and processing.

To maximize the impact of precision medicine approaches, it is critical to identify genetic variants underlying disease and to accurately quantify their functional effects. A gene exemplifying the challenge of variant interpretation is the von Hippel–Lindautumor suppressor ( VHL ). VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here we optimize and apply saturation genome editing to assay nearly all possible single-nucleotide variants (SNVs) across VHL ’s coding sequence. To delineate mechanisms, we quantify mRNA dosage effects and compare functional effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types and reveal new mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered clinically and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype–phenotype relationships across complete genes. Saturation genome editing characterizes von Hippel–Lindau ( VHL ) coding variants and their associations with diseases. Function scores for 2,268 VHL single-nucleotide variants (SNVs) classify pathogenic alleles driving renal cell carcinoma and suggest new mechanisms by which variants impact function.

Predicting how changes in human DNA sequence impact gene expression remains challenging. Here, we present PETRA (Prime Editing of Transcribed Regulatory elements to Assay expression), a multiplexed genome editing method to quantify the effects of regulatory variants at scale. PETRA leverages the delivery of variants to abundantly transcribed regions of genes such that sequence-specific effects on mRNA expression can be read out by amplicon sequencing. We demonstrate PETRA in Jurkat cells by scoring 13,935 six-nucleotide insertions delivered to the 5' untranslated regions (5' UTRs) of four genes important for T cell responses, namely VAV1, IL2RA, CD28 and OTUD7B. Effects on expression are linked to the creation of new transcription factor binding sites (TFBSs), as well as to alterations in splicing and translation initiation. Combinatorial delivery of TFBSs identified using PETRA generates alleles that increase mRNA expression more than 10-fold. Additionally, we extend PETRA to primary human T cells to compare effects across cell types and use our data to assess the performance of computational models. These results establish PETRA as a flexible means of dissecting and reprogramming the logic of gene regulation across genomic contexts and cell types.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Figure 5 added including new comparisons of PETRA scores to predictions from computational models. Supplemental files updates.Funder Information DeclaredEuropean Research Council, Seq2Func-NCThe Francis Crick Institute, https://ror.org/04tnbqb63, CC2190

Despite major advances in spatial RNA sequencing, the ability to extract temporal information in RNA sequencing experiments is still limited. Here, we describe Transcriptome Timestamping (T2), a system which harnesses naturally occurring A-to-I editing of RNA transcripts in unmodified human cells to infer transcriptional history. T2 provides age estimates for individual RNA transcripts, and serves as an endogenous molecular recorder, differentiating between complex transcriptional programs. We show that T2 can identify transient and transitional transcriptional programs in primary differentiating monocytes that are not apparent from gene expression analysis alone, including a regulatory module in the monocyte-to-macrophage transition that, to our knowledge, has not yet been described in humans. Finally, we show that T2 can also be applied to single cell data, allowing us to identify transcriptional programs in heterogeneous populations, such as asynchronously dividing cells. T2 is a scalable approach to temporal transcriptomics that can be applied to track the activity of thousands of genes in unmodified, primary human cells and tissues, with no genetic engineering.Competing Interest StatementA.G., J.B., A.W., D.M., G.Y. and S.G.R. are listed as inventors on UK patent application GB2400892.2. A.P.C. is a cofounder of Caeruleus Genomics Ltd and is an inventor on several patents related to sequencing technologies filed by Oxford University Innovations.

The design and implementation of single-cell experiments is often limited by their requirement for fresh starting material. We have adapted a method for histological tissue fixation using dithio-bis(succinimidyl propionate) (DSP), or Lomant's Reagent, to stabilise cell samples for single-cell transcriptomic applications. DSP is a reversible cross-linker of free amine groups that has previously been shown to preserve tissue integrity for histology while maintaining RNA integrity and yield in bulk RNA extractions. Although RNA-seq data from DSP-fixed single cells appears to be prone to characteristic artefacts, such as slightly reduced yield of cDNA and a detectable 3-prime bias in comparison with fresh cells, cell preservation using DSP does not appear to substantially reduce RNA complexity at the gene level. In addition, there is evidence that instantaneous fixation of cells can reduce inter-cell technical variability. The ability of DSP-fixed cells to retain commonly used dyes, such as propidium iodide, enables the tracking of experimental sub-populations and the recording of cell viability at the point of fixation. Preserving cells using DSP will remove several barriers in the staging of single-cell experiments, including the transport of samples and the scheduling of shared equipment for downstream single-cell isolation and processing.

The quantum circuit model is the de-facto way of designing quantum algorithms. Yet any level of abstraction away from the underlying hardware incurs overhead. In this work, we develop quantum algorithms for Hamiltonian simulation \"one level below” the circuit model, exploiting the underlying control over qubit interactions available in most quantum hardware and deriving analytic circuit identities for synthesising multi-qubit evolutions from two-qubit interactions. We then analyse the impact of these techniques under the standard error model where errors occur per gate, and an error model with a constant error rate per unit time. To quantify the benefits of this approach, we apply it to time-dynamics simulation of the 2D spin Fermi-Hubbard model. Combined with new error bounds for Trotter product formulas tailored to the non-asymptotic regime and an analysis of error propagation, we find that e.g. for a 5 × 5 Fermi-Hubbard lattice we reduce the circuit depth from 1, 243, 586 using the best previous fermion encoding and error bounds in the literature, to 3, 209 in the per-gate error model, or the circuit-depth-equivalent to 259 in the per-time error model. This brings Hamiltonian simulation, previously beyond reach of current hardware for non-trivial examples, significantly closer to being feasible in the NISQ era. The way quantum simulation algorithms are translated into specific hardware implementations often translates into additional overhead. Here, the authors improve the efficiency of Hamiltonian simulation using a method that allows efficient synthesis of multi-qubit evolutions from two-qubit interactions.

Determining the ground and excited state properties of materials is considered one of the most promising applications of quantum computers. On near-term hardware, the limiting constraint on such simulations is the requisite circuit depths and qubit numbers, which currently lie well beyond near-term capabilities. Here we develop a quantum algorithm which reduces the estimated cost of material simulations. For example, we obtain a circuit depth improvement by up to 6 orders of magnitude for a Trotter layer of time-dynamics simulation in the transition-metal oxide SrVO 3 compared with the best previous quantum algorithms. We achieve this by introducing a collection of connected techniques, including highly localised and physically compact representations of materials Hamiltonians in the Wannier basis, a hybrid fermion-to-qubit mapping, and an efficient circuit compiler. Combined together, these methods leverage locality of materials Hamiltonians and result in a design that generates quantum circuits with depth independent of the system’s size. Although the requisite resources for the quantum simulation of materials are still beyond current hardware, our results show that realistic simulation of specific properties may be feasible without necessarily requiring fully scalable, fault-tolerant quantum computers, providing quantum algorithm design incorporates deeper understanding of the target materials and applications. The use of NISQ devices for useful quantum simulations of materials and chemistry is still mainly limited by the necessary circuit depth. Here, the authors propose to combine classically-generated effective Hamiltonians, hybrid fermion-to-qubit mapping and circuit optimisations to bring this requirement closer to experimental feasibility.

In this work we investigate a binned version of quantum phase estimation (QPE) set out by Somma (2019 New J. Phys. 21 123025) and known as the quantum eigenvalue estimation problem (QEEP). Specifically, we determine whether the circuit decomposition techniques we set out in previous work, Clinton et al (2021 Nat. Commun. 12 1–10), can improve the performance of QEEP in the noisy intermediate scale quantum (NISQ) regime. To this end we adopt a physically motivated abstraction of NISQ device capabilities as in Clinton et al (2021 Nat. Commun. 12 1–10). Within this framework, we find that our techniques reduce the threshold at which it becomes possible to perform the minimum two-bin instance of this algorithm by an order of magnitude. This is for the specific example of a two dimensional spin Fermi-Hubbard model. For example, we estimate that the depolarizing single qubit error rate required to implement a minimum two bin example of QEEP—with a 5 × 5 Fermi-Hubbard model and up to a precision of 10 % —can be reduced from 10 −7 to 10 −5 . We explore possible modifications to this protocol and propose an application, which we dub randomized quantum eigenvalue estimation problem (rQEEP). rQEEP outputs estimates on the fraction of eigenvalues which lie within randomly chosen bins and upper bounds the total deviation of these estimates from the true values. One use case we envision for this algorithm is resolving density of states features of local Hamiltonians.

Lassa virus (LASV), a mammarenavirus, infects an estimated 100,000–300,000 individuals yearly in western Africa and frequently causes lethal disease. Currently, no LASV-specific antivirals or vaccines are commercially available for prevention or treatment of Lassa fever, the disease caused by LASV. The development of medical countermeasure screening platforms is a crucial step to yield licensable products. Using reverse genetics, we generated a recombinant wild-type LASV (rLASV-WT) and a modified version thereof encoding a cleavable green fluorescent protein (GFP) as a reporter for rapid and quantitative detection of infection (rLASV-GFP). Both rLASV-WT and wild-type LASV exhibited similar growth kinetics in cultured cells, whereas growth of rLASV-GFP was slightly impaired. GFP reporter expression by rLASV-GFP remained stable over several serial passages in Vero cells. Using two well-characterized broad-spectrum antivirals known to inhibit LASV infection, favipiravir and ribavirin, we demonstrate that rLASV-GFP is a suitable screening tool for the identification of LASV infection inhibitors. Building on these findings, we established a rLASV-GFP-based high-throughput drug discovery screen and an rLASV-GFP-based antibody neutralization assay. Both platforms, now available as a standard tool at the IRF-Frederick (an international resource), will accelerate anti-LASV medical countermeasure discovery and reduce costs of antiviral screens in maximum containment laboratories.