scResolve: Recovering single cell expression profiles from multi-cellular spatial transcriptomics.

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable from cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Characterization of cell cycle and apoptosis in Chinese hamster ovary cell culture using flow cytometry for bioprocess monitoring.

Chinese hamster ovary (CHO) cells are by far the most important mammalian cell lines used for producing antibodies and other therapeutic proteins. It is critical to fully understand their physiological conditions during a bioprocess in order to achieve the highest productivity and the desired product quality. Flow cytometry technology possesses unique advantages for measuring multiple cellular attributes for a given cell and examining changes in cell culture heterogeneity over time that can be used as metrics for enhanced process understanding and control strategy. Flow cytometry-based assays were utilized to examine the progression of cell cycle and apoptosis in three case studies using different antibody-producing CHO cell lines in both fed-batch and perfusion bioprocesses. In our case studies, we found that G0/G1 phase distribution and early apoptosis accumulation responded to subtle changes in culture conditions, such as pH shifting or momentary glucose depletion. In a perfusion process, flow cytometry provided an insightful understanding of the cell physiological status under a hypothermic condition. More importantly, these changes in cell cycle and apoptosis were not detected by a routine trypan blue exclusion-based cell counting and viability measurement. In summary, integration of flow cytometry into bioprocesses as a process analytical technology tool can be beneficial for establishing optimum process conditions and process control.

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer.

PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin-stained frozen tissue sections of SLNs in breast cancer patients. MATERIALS AND METHODS: A total of 297 digital slides were obtained from frozen SLN sections, which include post-neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). RESULTS: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. CONCLUSION: In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.

Modulating Responses of Toehold Switches by an Inhibitory Hairpin.

The toehold switch consists of a cis-repressing switch RNA hairpin and a trans-acting trigger RNA. The binding of the trigger RNA to an unpaired toehold sequence of the switch hairpin allows for a branch migration process, exposing the start codon and ribosome binding site for translation initiation. In this work, we demonstrate that responses of toehold switches can be modulated by introducing an inhibitory hairpin that shortens the length of the unpaired toehold region. First, we investigated the effect of the toehold region length on output gene expression and showed that the second trigger RNA, which binds to the inhibitory hairpin, is necessary for output gene activation when the hairpin-to-hairpin spacing is short. Second, the apparent Hill coefficient was found generally to increase with decreasing hairpin-to-hairpin spacing or increasing hairpin number. This work expands the utility of toehold switches by providing a new way to modulate their response.

Establishing a Multivariate Model for Predictable Antisense RNA-Mediated Repression.

Recent advances in our understanding of RNA folding and functions have facilitated the use of regulatory RNAs such as synthetic antisense RNAs (asRNAs) to modulate gene expression. However, despite the simple and universal complementarity rule, predictable asRNA-mediated repression is still challenging due to the intrinsic complexity of native asRNA-mediated gene regulation. To address this issue, we present a multivariate model, based on the change in free energy of complex formation (Δ GCF) and percent mismatch of the target binding region, which can predict synthetic asRNA-mediated repression efficiency in diverse contexts. First, 69 asRNAs that bind to multiple target mRNAs were designed and tested to create the predictive model. Second, we showed that the same model is effective predicting repression of target genes in both plasmids and chromosomes. Third, using our model, we designed asRNAs that simultaneously modulated expression of a toxin and its antitoxin to demonstrate tunable control of cell growth. Fourth, we tested and validated the same model in two different biotechnologically important organisms: Escherichia coli Nissle 1917 and Bacillus subtilis 168. Last, multiple parameters, including target locations, the presence of an Hfq binding site, GC contents, and gene expression levels, were revisited to define the conditions under which the multivariate model should be used for accurate prediction. Together, 434 different strain-asRNA combinations were tested, validating the predictive model in a variety of contexts, including multiple target genes and organisms. The result presented in this study is an important step toward achieving predictable tunability of asRNA-mediated repression.

Multilevel Regulation of Bacterial Gene Expression with the Combined STAR and Antisense RNA System.

Synthetic small RNA regulators have emerged as a versatile tool to predictably control bacterial gene expression. Owing to their simple design principles, small size, and highly orthogonal behavior, these engineered genetic parts have been incorporated into genetic circuits. However, efforts to achieve more sophisticated cellular functions using RNA regulators have been hindered by our limited ability to integrate different RNA regulators into complex circuits. Here, we present a combined RNA regulatory system in Escherichia coli that uses small transcription activating RNA (STAR) and antisense RNA (asRNA) to activate or deactivate target gene expression in a programmable manner. Specifically, we demonstrated that the activated target output by the STAR system can be deactivated by expressing two different types of asRNAs: one binds to and sequesters the STAR regulator, affecting the transcription process, while the other binds to the target mRNA, affecting the translation process. We improved deactivation efficiencies (up to 96%) by optimizing each type of asRNA and then integrating the two optimized asRNAs into a single circuit. Furthermore, we demonstrated that the combined STAR and asRNA system can control gene expression in a reversible way and can regulate expression of a gene in the genome. Lastly, we constructed and simultaneously tested two A AND NOT B logic gates in the same cell to show sophisticated multigene regulation by the combined system. Our approach establishes a methodology for integrating multiple RNA regulators to rationally control multiple genes.

Design rules of synthetic non-coding RNAs in bacteria.

One of the long-term goals of synthetic biology is to develop designable genetic parts with predictable behaviors that can be utilized to implement diverse cellular functions. The discovery of non-coding RNAs and their importance in cellular processing have rapidly attracted researchers' attention towards designing functional non-coding RNA molecules. These synthetic non-coding RNAs have simple design principles governed by Watson-Crick base pairing, but exhibit increasingly complex functions. Importantly, due to their specific and modular behaviors, synthetic non-coding RNAs have been widely adopted to modulate transcription and translation of target genes. In this review, we summarize various design rules and strategies employed to engineer synthetic non-coding RNAs. Specifically, we discuss how RNA molecules can be transformed into powerful regulators and utilized to control target gene expression. With the establishment of generalizable non-coding RNA design rules, the research community will shift its focus to RNA regulators from protein regulators.

Programmable control of bacterial gene expression with the combined CRISPR and antisense RNA system.

A central goal of synthetic biology is to implement diverse cellular functions by predictably controlling gene expression. Though research has focused more on protein regulators than RNA regulators, recent advances in our understanding of RNA folding and functions have motivated the use of RNA regulators. RNA regulators provide an advantage because they are easier to design and engineer than protein regulators, potentially have a lower burden on the cell and are highly orthogonal. Here, we combine the CRISPR system from Streptococcus pyogenes and synthetic antisense RNAs (asRNAs) in Escherichia coli strains to repress or derepress a target gene in a programmable manner. Specifically, we demonstrate for the first time that the gene target repressed by the CRISPR system can be derepressed by expressing an asRNA that sequesters a small guide RNA (sgRNA). Furthermore, we demonstrate that tunable levels of derepression can be achieved (up to 95%) by designing asRNAs that target different regions of a sgRNA and by altering the hybridization free energy of the sgRNA-asRNA complex. This new system, which we call the combined CRISPR and asRNA system, can be used to reversibly repress or derepress multiple target genes simultaneously, allowing for rational reprogramming of cellular functions.

Robust, tunable genetic memory from protein sequestration combined with positive feedback.

Natural regulatory networks contain many interacting components that allow for fine-tuning of switching and memory properties. Building simple bistable switches, synthetic biologists have learned the design principles of complex natural regulatory networks. However, most switches constructed so far are so simple (e.g. comprising two regulators) that they are functional only within a limited parameter range. Here, we report the construction of robust, tunable bistable switches in Escherichia coli using three heterologous protein regulators (ExsADC) that are sequestered into an inactive complex through a partner swapping mechanism. On the basis of mathematical modeling, we accurately predict and experimentally verify that the hysteretic region can be fine-tuned by controlling the interactions of the ExsADC regulatory cascade using the third member ExsC as a tuning knob. Additionally, we confirm that a dual-positive feedback switch can markedly increase the hysteretic region, compared to its single-positive feedback counterpart. The dual-positive feedback switch displays bistability over a 10(6)-fold range of inducer concentrations, to our knowledge, the largest range reported so far. This work demonstrates the successful interlocking of sequestration-based ultrasensitivity and positive feedback, a design principle that can be applied to the construction of robust, tunable, and predictable genetic programs to achieve increasingly sophisticated biological behaviors.