High-throughput tagging of endogenous loci for rapid characterization of protein function.

To facilitate the interrogation of protein function at scale, we have developed high-throughput insertion of tags across the genome (HITAG). HITAG enables users to rapidly produce libraries of cells, each with a different protein of interest C-terminally tagged. HITAG is based on a modified strategy for performing Cas9-based targeted insertions, coupled with an improved approach for selecting properly tagged lines. Analysis of the resulting clones generated by HITAG reveals high tagging specificity, with most successful tagging events being indel free. Using HITAG, we fuse mCherry to a set of 167 stress granule-associated proteins and elucidate the features that drive a subset of proteins to strongly accumulate within these transient RNA-protein granules.

PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation.

Some robust point cloud registration approaches with controllable pose refinement magnitude, such as ICP and its variants, are commonly used to improve 6D pose estimation accuracy. However, the effectiveness of these methods gradually diminishes with the advancement of deep learning techniques and the enhancement of initial pose accuracy, primarily due to their lack of specific design for pose refinement. In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation. The pipeline consists of two steps. First, it completes the input point clouds via a novel pose-sensitive point completion network. The network uses both local and global features with pose information during point completion. Then, it registers the completed object point cloud with the corresponding target point cloud by our proposed Color supported Iterative KeyPoint (CIKP) method. The CIKP method introduces color information into registration and registers a point cloud around each keypoint to increase stability. The PCKRF pipeline can be integrated with existing popular 6D pose estimation methods, such as the full flow bidirectional fusion network, to further improve their pose estimation accuracy. Experiments demonstrate that our method exhibits superior stability compared to existing approaches when optimizing initial poses with relatively high precision. Notably, the results indicate that our method effectively complements most existing pose estimation techniques, leading to improved performance in most cases. Furthermore, our method achieves promising results even in challenging scenarios involving textureless and symmetrical objects. Our source code is available at https://github.com/zhanhz/KRF.

Functional map of SARS-CoV-2 3CL protease reveals tolerant and immutable sites.

The SARS-CoV-2 3CL protease (3CLpro) is an attractive therapeutic target, as it is essential to the virus and highly conserved among coronaviruses. However, our current understanding of its tolerance to mutations is limited. Here, we develop a yeast-based deep mutational scanning approach to systematically profile the activity of all possible single mutants of the 3CLpro and validate a subset of our results within authentic viruses. We reveal that the 3CLpro is highly malleable and is capable of tolerating mutations throughout the protein. Yet, we also identify specific residues that appear immutable, suggesting that these may be targets for future 3CLpro inhibitors. Finally, we utilize our screening as a basis to identify E166V as a resistance-conferring mutation against the clinically used 3CLpro inhibitor, nirmatrelvir. Collectively, the functional map presented herein may serve as a guide to better understand the biological properties of the 3CLpro and for drug development against coronaviruses.

The Functional Landscape of SARS-CoV-2 3CL Protease.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) as the etiologic agent of COVID-19 (coronavirus disease 2019) has drastically altered life globally. Numerous efforts have been placed on the development of therapeutics to treat SARS-CoV-2 infection. One particular target is the 3CL protease (3CL pro ), which holds promise as it is essential to the virus and highly conserved among coronaviruses, suggesting that it may be possible to find broad inhibitors that treat not just SARS-CoV-2 but other coronavirus infections as well. While the 3CL protease has been studied by many groups for SARS-CoV-2 and other coronaviruses, our understanding of its tolerance to mutations is limited, knowledge which is particularly important as 3CL protease inhibitors become utilized clinically. Here, we develop a yeast-based deep mutational scanning approach to systematically profile the activity of all possible single mutants of the SARS-CoV-2 3CL pro , and validate our results both in yeast and in authentic viruses. We reveal that the 3CL pro is highly malleable and is capable of tolerating mutations throughout the protein, including within the substrate binding pocket. Yet, we also identify specific residues that appear immutable for function of the protease, suggesting that these interactions may be novel targets for the design of future 3CL pro inhibitors. Finally, we utilize our screening results as a basis to identify E166V as a resistance-conferring mutation against the therapeutic 3CL pro inhibitor, nirmatrelvir, in clinical use. Collectively, the functional map presented herein may serve as a guide for further understanding of the biological properties of the 3CL protease and for drug development for current and future coronavirus pandemics.

Quantifying protein abundance on single cells using split-pool sequencing on DNA-barcoded antibodies for diagnostic applications.

Proteins play critical roles across all facets of biology, with their abundance frequently used as markers of cell identity and state. The most popular method for detecting proteins on single cells, flow cytometry, is limited by considerations of fluorescent spectral overlap. While mass cytometry (CyTOF) allows for the detection of upwards of 40 epitopes simultaneously, it requires local access to specialized instrumentation not commonly accessible to many laboratories. To overcome these limitations, we independently developed a method to quantify multiple protein targets on single cells without the need for specialty equipment other than access to widely available next generation sequencing (NGS) services. We demonstrate that this combinatorial indexing method compares favorably to traditional flow-cytometry, and allows over two dozen target proteins to be assayed at a time on single cells. To showcase the potential of the technique, we analyzed peripheral blood and bone marrow aspirates from human clinical samples, and identified pathogenic cellular subsets with high fidelity. The ease of use of this technique makes it a promising technology for high-throughput proteomics and for interrogating complex samples such as those from patients with leukemia.

SCOPE-Seq: a scalable technology for linking live cell imaging and single-cell RNA sequencing.

Optically decodable beads link the identity of a sample to a measurement through an optical barcode, enabling libraries of biomolecules to be captured on beads in solution and decoded by fluorescence. This approach has been foundational to microarray, sequencing, and flow-based expression profiling technologies. We combine microfluidics with optically decodable beads and show that phenotypic analysis of living cells can be linked to single-cell sequencing. As a proof-of-concept, we demonstrate the accuracy and scalability of our tool called Single Cell Optical Phenotyping and Expression sequencing (SCOPE-Seq) to combine live cell imaging with single-cell RNA sequencing.

Ultraparamagnetic Cells Formed through Intracellular Oxidation and Chelation of Paramagnetic Iron.

Making cells magnetic is a long-standing goal of chemical biology, aiming to enable the separation of cells from complex biological samples and their visualization in vivo using magnetic resonance imaging (MRI). Previous efforts towards this goal, focused on engineering cells to biomineralize superparamagnetic or ferromagnetic iron oxides, have been largely unsuccessful due to the stringent required chemical conditions. Here, we introduce an alternative approach to making cells magnetic, focused on biochemically maximizing cellular paramagnetism. We show that a novel genetic construct combining the functions of ferroxidation and iron chelation enables engineered bacterial cells to accumulate iron in "ultraparamagnetic" macromolecular complexes, allowing these cells to be trapped with magnetic fields and imaged with MRI in vitro and in vivo. We characterize the properties of these cells and complexes using magnetometry, nuclear magnetic resonance, biochemical assays, and computational modeling to elucidate the unique mechanisms and capabilities of this paramagnetic concept.