Biological signal processing filters via engineering allosteric transcription factors

Signal processing is critical to a myriad of biological phenomena (natural and engineered) that involve gene regulation. Biological signal processing can be achieved by way of allosteric transcription factors. In canonical regulatory systems (e.g., the lactose repressor), an INPUT signal results in the induction of a given transcription factor and objectively switches gene expression from an OFF state to an ON state. In such biological systems, to revert the gene expression back to the OFF state requires the aggressive dilution of the input signal, which can take 1 or more d to achieve in a typical biotic system. In this study, we present a class of engineered allosteric transcription factors capable of processing two-signal INPUTS, such that a sequence of INPUTS can rapidly transition gene expression between alternating OFF and ON states. Here, we present two fundamental biological signal processing filters, BANDPASS and BANDSTOP, that are regulated by D-fucose and isopropyl-β-D-1-thiogalactopyranoside. BANDPASS signal processing filters facilitate OFF–ON–OFF gene regulation. Whereas, BANDSTOP filters facilitate the antithetical gene regulation, ON–OFF–ON. Engineered signal processing filters can be directed to seven orthogonal promoters via adaptive modular DNA binding design. This collection of signal processing filters can be used in collaboration with our established transcriptional programming structure. Kinetic studies show that our collection of signal processing filters can switch between states of gene expression within a few minutes with minimal metabolic burden—representing a paradigm shift in general gene regulation.

Mapbatch: Conservative Batch Normalization for Single Cell RNA-Sequencing Data Enables Discovery of Rare Cell Populations in a Multiple Myeloma Cohort

Introduction Many cancers involve the participation of rare cell populations that may only be found in a subset of patients. Single-cell RNA sequencing (scRNA-seq) can identify distinct cell populations across multiple samples with batch normalization used to reduce processing-based effects between samples. However, aggressive normalization obscures rare cell populations, which may be erroneously grouped with other cell types. There is a need for conservative batch normalization that maintains the biological signal necessary to detect rare cell populations. MapBatch We designed a batch normalization tool, MapBatch, based on two principles: an autoencoder trained with a single sample learns the underlying gene expression structure of cell types without batch effect; and an ensemble model combines multiple autoencoders, allowing the use of multiple samples for training. Each autoencoder is trained on one sample, learning a projection into the biological space S representing the real expression differences between cells in that sample (Figure 1a, middle). When other samples are projected into S, the projection reduces expression differences orthogonal to S, while preserving differences along S. The reverse projection transforms the data back into gene space at the autoencoder's output, sans expression differences orthogonal to S (Figure 1a, right). Since batch-based technical differences are not represented in S, this transformation selectively removes batch effect between samples, while preserving biological signal. The autoencoder output thus represents normalized expression data, conditioned on the training sample. To incorporate multiple samples into training, MapBatch uses an ensemble of autoencoders, each trained with a single sample (Figure 1b). We train with a minimal number of samples necessary to cover the different cell populations in the dataset. We implement regularization using dropout and noise layers, and an a priori feature extraction layer using KEGG gene modules. The autoencoders' outputs are concatenated for downstream analysis. For visualization and clustering, we use the top principal components of the concatenated outputs. For differential expression (DE), we perform DE on each of the gene matrices output by each model, then take the result with the lowest P-value. To test MapBatch, we generated a synthetic dataset based on 7 batches of publicly available PBMC data. For each batch we simulated rare cell populations by selecting one of three cell types to perturb by up and down-regulating 40 genes in 0.5%-2% of the cells (Figure 1c). We simulated additional batch effect by scaling each gene in each batch with a scaling factor. Upon visualization and clustering, cells grouped largely by batch (Figure 1d). After batch normalization, cells grouped by cell type rather than batch, and all three perturbed cell populations were successfully delineated (Figure 1e). DE between each perturbed population and its mother cells accurately retrieved the perturbed genes, showing that normalization maintained real expression differences (Figure 1e). In contrast, three methods tested Seurat (Stuart et al., 2019), Harmony (Korsunsky et al., 2019), and Liger (Welch et al., 2019) could only derive a subset of the perturbed populations (Figures 1f-h). MapBatch identifies rare populations in multiple myeloma (MM) We used MapBatch to process bone marrow scRNA-seq data from 14 MM samples and 2 healthy controls. After batch normalization, unsupervised clustering identified 20 clusters, which we annotated using MapCell (Koh & Hoon, 2019) (Figures 2a, 2b). We identified 3 small clusters of cells that could not be reliably annotated, comprising less than 1% of total cells and found in only a subset of patients (Figures 2c, 2d). As validation, we observed that these cells were present in distinct clusters in individual samples using their uncorrected expression data, providing evidence that these clusters were not driven by batch effect nor MapBatch (Figure 2e). Conclusion Batch normalization of scRNA-seq data involves a trade-off between minimizing batch effect and maximizing the remaining biological signal. While most methods lean towards the former, MapBatch maintains more biological signal for downstream analysis, enabling the discovery of previously difficult to find cell populations. Figure 1 Figure 1. Disclosures Xu: Proteona Pte Ltd: Current Employment. Scolnick: Proteona Pte Ltd: Current holder of individual stocks in a privately-held company. Huo: Proteona Pte Ltd: Ended employment in the past 24 months. Lovci: Proteona Pte Ltd: Current Employment. Chng: Amgen: Honoraria, Research Funding; Abbvie: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; Celgene: Honoraria, Research Funding.

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal. We compare our method to previous approaches on a large-scale dataset of two-photon imaging recordings of retinal bipolar cell responses to visual stimuli. This dataset provides a unique benchmark as it contains biological signal from well-defined cell types that is obscured by large inter-experimental variability. In a supervised setting, we compare the generalization performance of cell type classifiers across experiments, which we validate with anatomical cell type distributions from electron microscopy data. In an unsupervised setting, we remove inter-experimental variability from data which can then be fed into arbitrary downstream analyses. In both settings, we find that our method achieves the best trade-off between removing inter-experimental variability and preserving biological signal. Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience.

Ultra-Weak Cell Radiation as an Impulse to Activate Biological Signal Transmission Paths

Basic physical research at the beginning of the 20th century developed concepts of the energetic properties of atoms and molecules with quantum mechanics, which increasingly also included biological structures. Considerations of a “charge transfer” or also known as “donor-acceptor interactions” of the movement of electrons between molecular structures developed. This energetic process is the basis of the ultra-weak cell radiation, which is to be discussed as the basis for the activation of the molecular signal transmission.