Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space.

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.

Towards maximum acceleration of monoclonal antibody development: Leveraging transposase-mediated cell line generation to enable GMP manufacturing within 3 months using a stable pool.

In recent years, acceleration of development timelines has become a major focus within the biopharmaceutical industry to bring innovative therapies faster to patients. However, in order to address a high unmet medical need even faster further acceleration potential has to be identified to transform "speed-to-clinic" concepts into "warp-speed" development programs. Recombinant Chinese hamster ovary (CHO) cell lines are the predominant expression system for monoclonal antibodies (mAbs) and are routinely generated by random transgene integration (RTI) of the genetic information into the host cell genome. This process, however, exhibits considerable challenges such as the requirement for a time-consuming clone screening process to identify a suitable clonally derived manufacturing cell line. Hence, RTI represents an error prone and tedious method leading to long development timelines until availability of Good Manufacturing Practice (GMP)-grade drug substance (DS). Transposase-mediated semi-targeted transgene integration (STI) has been recently identified as a promising alternative to RTI as it allows for a more rapid generation of high-performing and stable production cell lines. In this report, we demonstrate how a STI technology was leveraged to develop a very robust DS manufacturing process based on a stable pool cell line at unprecedented pace. Application of the novel strategy resulted in the manufacturing of GMP-grade DS at 2,000 L scale in less than three months paving the way for a start of Phase I clinical trials only six months after transfection. Finally, using a clonally derived production cell line, which was established from the parental stable pool, we were able to successfully implement a process with an increased mAb titer of up to 5 g per liter at the envisioned commercial scale (12,000 L) within eight months.

Multiscale PHATE identifies multimodal signatures of COVID-19.

As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16hiCD66blo neutrophil and IFN-γ+ granzyme B+ Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.

Rapid cGMP manufacturing of COVID-19 monoclonal antibody using stable CHO cell pools.

Therapeutic proteins, including monoclonal antibodies, are typically manufactured using clonally derived, stable host cell lines, since consistent and predictable cell culture performance is highly desirable. However, selecting and preparing banks of stable clones takes considerable time, which inevitably extends overall development timelines for new therapeutics by delaying the start of subsequent activities, such as the scale-up of manufacturing processes. In the context of the coronavirus disease 2019 (COVID-19) pandemic, with its intense pressure for accelerated development strategies, we used a novel transposon-based Leap-In Transposase® system to rapidly generate high-titer stable pools and then used them directly for large scale-manufacturing of an anti-severe acute respiratory syndrome coronavirus 2 monoclonal antibody under cGMP. We performed the safety testing of our non-clonal cell bank, then used it to produce material at a 200L-scale for preclinical safety studies and formulation development work, and thereafter at 2000L scale for supply of material for a Phase 1 clinical trial. Testing demonstrated the comparability of critical product qualities between the two scales and, more importantly, that our final clinical trial product met all pre-set product quality specifications. The above expediated approach provided clinical trial material within 4.5 months, in comparison to 12-14 months for production of clinical trial material via the conventional approach.

Inducible protein expression in piggyBac transposase mediated stable HEK293 cell pools.

Described here is the use of piggyBac transposase generated HEK293 stable cell pools for doxycycline-inducible protein production. The key benefits of the system are that low amounts of plasmid DNA are needed for transfection, high levels of protein expression can be achieved also for toxic proteins at robust scalability and reproducibility and the recombinant cell line can be stored as frozen cell bank. Transfection, selection, expression and purification of enhanced green fluorescence protein (eGFP) and SARS-CoV-2 Spike protein are described in this chapter.

Epigenetic Landscapes of Single-Cell Chromatin Accessibility and Transcriptomic Immune Profiles of T Cells in COVID-19 Patients.

T cells play a critical role in coronavirus diseases. How they do so in COVID-19 may be revealed by analyzing the epigenetic chromatin accessibility of cis- and trans-regulatory elements and creating transcriptomic immune profiles. We performed single-cell assay for transposase-accessible chromatin (scATAC) and single-cell RNA (scRNA) sequencing (seq) on the peripheral blood mononuclear cells (PBMCs) of severely ill/critical patients (SCPs) infected with COVID-19, moderate patients (MPs), and healthy volunteer controls (HCs). About 76,570 and 107,862 single cells were used, respectively, for analyzing the characteristics of chromatin accessibility and transcriptomic immune profiles by the application of scATAC-seq (nine cases) and scRNA-seq (15 cases). The scATAC-seq detected 28,535 different peaks in the three groups; among these peaks, 41.6 and 10.7% were located in the promoter and enhancer regions, respectively. Compared to HCs, among the peak-located genes in the total T cells and its subsets, CD4+ T and CD8+ T cells, from SCPs and MPs were enriched with inflammatory pathways, such as mitogen-activated protein kinase (MAPK) signaling pathway and tumor necrosis factor (TNF) signaling pathway. The motifs of TBX21 were less accessible in the CD4+ T cells of SCPs compared with those in MPs. Furthermore, the scRNA-seq showed that the proportion of T cells, especially the CD4+ T cells, was decreased in SCPs and MPs compared with those in HCs. Transcriptomic results revealed that histone-related genes, and inflammatory genes, such as NFKBIA, S100A9, and PIK3R1, were highly expressed in the total T cells, CD4+ T and CD8+ T cells, both in the cases of SCPs and MPs. In the CD4+ T cells, decreased T helper-1 (Th1) cells were observed in SCPs and MPs. In the CD8+T cells, activation markers, such as CD69 and HLA class II genes (HLA-DRA, HLA-DRB1, and HLA-DRB5), were significantly upregulated in SCPs. An integrated analysis of the data from scATAC-seq and scRNA-seq showed some consistency between the approaches. Cumulatively, we have generated a landscape of chromatin epigenetic status and transcriptomic immune profiles of T cells in patients with COVID-19. This has provided a deeper dissection of the characteristics of the T cells involved at a higher resolution than from previously obtained data merely by the scRNA-seq analysis. Our data led us to suggest that the T-cell inflammatory states accompanied with defective functions in the CD4+ T cells of SCPs may be the key factors for determining the pathogenesis of and recovery from COVID-19.