ScSmOP: a universal computational pipeline for single-cell single-molecule multiomics data analysis.

Single-cell multiomics techniques have been widely applied to detect the key signature of cells. These methods have achieved a single-molecule resolution and can even reveal spatial localization. These emerging methods provide insights elucidating the features of genomic, epigenomic and transcriptomic heterogeneity in individual cells. However, they have given rise to new computational challenges in data processing. Here, we describe Single-cell Single-molecule multiple Omics Pipeline (ScSmOP), a universal pipeline for barcode-indexed single-cell single-molecule multiomics data analysis. Essentially, the C language is utilized in ScSmOP to set up spaced-seed hash table-based algorithms for barcode identification according to ligation-based barcoding data and synthesis-based barcoding data, followed by data mapping and deconvolution. We demonstrate high reproducibility of data processing between ScSmOP and published pipelines in comprehensive analyses of single-cell omics data (scRNA-seq, scATAC-seq, scARC-seq), single-molecule chromatin interaction data (ChIA-Drop, SPRITE, RD-SPRITE), single-cell single-molecule chromatin interaction data (scSPRITE) and spatial transcriptomic data from various cell types and species. Additionally, ScSmOP shows more rapid performance and is a versatile, efficient, easy-to-use and robust pipeline for single-cell single-molecule multiomics data analysis.

MCI-frcnn: A deep learning method for topological micro-domain boundary detection.

Chromatin structural domains, or topologically associated domains (TADs), are a general organizing principle in chromatin biology. RNA polymerase II (RNAPII) mediates multiple chromatin interactive loops, tethering together as RNAPII-associated chromatin interaction domains (RAIDs) to offer a framework for gene regulation. RAID and TAD alterations have been found to be associated with diseases. They can be further dissected as micro-domains (micro-TADs and micro-RAIDs) by clustering single-molecule chromatin-interactive complexes from next-generation three-dimensional (3D) genome techniques, such as ChIA-Drop. Currently, there are few tools available for micro-domain boundary identification. In this work, we developed the MCI-frcnn deep learning method to train a Faster Region-based Convolutional Neural Network (Faster R-CNN) for micro-domain boundary detection. At the training phase in MCI-frcnn, 50 images of RAIDs from Drosophila RNAPII ChIA-Drop data, containing 261 micro-RAIDs with ground truth boundaries, were trained for 7 days. Using this well-trained MCI-frcnn, we detected micro-RAID boundaries for the input new images, with a fast speed (5.26 fps), high recognition accuracy (AUROC = 0.85, mAP = 0.69), and high boundary region quantification (genomic IoU = 76%). We further applied MCI-frcnn to detect human micro-TADs boundaries using human GM12878 SPRITE data and obtained a high region quantification score (mean gIoU = 85%). In all, the MCI-frcnn deep learning method which we developed in this work is a general tool for micro-domain boundary detection.

MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification.

The emerging ligation-free three-dimensional (3D) genome mapping technologies can identify multiplex chromatin interactions with single-molecule precision. These technologies not only offer new insight into high-dimensional chromatin organization and gene regulation, but also introduce new challenges in data visualization and analysis. To overcome these challenges, we developed MCIBox, a toolkit for multi-way chromatin interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes. MCIBox is based on various clustering algorithms integrated with dimensionality reduction methods that can display multiplex chromatin interactions at single-molecule level, allowing users to explore chromatin extrusion patterns and super-enhancers regulation modes in transcription, and to identify single-molecule chromatin complexes that are clustered into micro-domains. Furthermore, MCIBox incorporates a two-dimensional kernel density estimation algorithm to identify micro-domains boundaries automatically. These micro-domains were stratified with distinctive signatures of transcription activity and contained different cell-cycle-associated genes. Taken together, MCIBox represents an invaluable tool for the study of multiple chromatin interactions and inaugurates a previously unappreciated view of 3D genome structure.

Metabolic Analysis Reveals Cry1C Gene Transformation Does Not Affect the Sensitivity of Rice to Rice Dwarf Virus.

Metabolomics is beginning to be used for assessing unintended changes in genetically modified (GM) crops. To investigate whether Cry1C gene transformation would induce metabolic changes in rice plants, and whether the metabolic changes would pose potential risks when Cry1C rice plants are exposed to rice dwarf virus (RDV), the metabolic profiles of Cry1C rice T1C-19 and its non-Bt parental rice MH63 under RDV-free and RDV-infected status were analyzed using gas chromatography-mass spectrometry (GC-MS). Compared to MH63 rice, slice difference was detected in T1C-19 under RDV-free conditions (less than 3%), while much more metabolites showed significant response to RDV infection in T1C-19 (15.6%) and in MH63 (5.0%). Pathway analysis showed biosynthesis of lysine, valine, leucine, and isoleucine may be affected by RDV infection in T1C-19. No significant difference in the contents of free amino acids (AAs) was found between T1C-19 and MH63 rice, and the free AA contents of the two rice plants showed similar responses to RDV infection. Furthermore, no significant differences of the RDV infection rates between T1C-19 and MH63 were detected. Our results showed the Cry1C gene transformation did not affect the sensitivity of rice to RDV, indicating Cry1C rice would not aggravate the epidemic and dispersal of RDV.

Cry1C rice doesn't affect the ecological fitness of rice brown planthopper, Nilaparvata lugens either under RDV stress or not.

The potential risks of Bt rice on non-target arthropods (NTAs) should be evaluated and defined before commercial production. Recently, effects of Bt rice on NTAs under abiotic and biotic stress conditions attracted much attention. Here we reported the effects of Bt rice T1C-19 (Cry1C rice) on the non-target herbivore, Nilaparvata lugens (rice brown planthopper, BPH) with or without RDV (rice dwarf virus) infection conditions. BPH showed no feeding and oviposition preference between Bt rice T1C-19 and its non-Bt parental rice Minghui 63 (MH63), as well as between RDV-infected and RDV-free rice plants. Meanwhile, rice type, RDV infection status, and their interaction had little impacts on the survival, development and fecundity of BPH. By comparison with non-Bt control, Bt rice T1C-19 with or without RDV infection had no significant effects on the life-table parameters of BPH including rm, R0, T, DT and λ. Thus, it could be concluded that Bt rice T1C-19 doesn't affect the ecological fitness of BPH either under RDV stress or not.

Comparing Gene Expression Profiles Between Bt and non-Bt Rice in Response to Brown Planthopper Infestation.

Bt proteins are the most widely used insecticidal proteins in transgenic crops for improving insect resistance. We previously observed longer nymphal developmental duration and lower fecundity in brown planthopper (BPH) fed on Bt rice line KMD2, although Bt insecticidal protein Cry1Ab could rarely concentrate in this non-target rice pest. In the present study, we performed microarray analysis in an effort to detect Bt-independent variation, which might render Bt rice more defensive and/or less nutritious to BPH. We detected 3834 and 3273 differentially expressed probe-sets in response to BPH infestation in non-Bt parent Xiushui 11 and Bt rice KMD2, respectively, only 439 of which showed significant differences in expression between rice lines. Our analysis revealed a shift from growth to defense responses in response to BPH infestation, which was also detected in many other studies of plants suffering biotic and abiotic stresses. Chlorophyll biosynthesis and basic metabolism pathways were inhibited in response to infestation. IAA and GA levels decreased as a result of the repression of biosynthesis-related genes or the induction of inactivation-related genes. In accordance with these observations, a number of IAA-, GA-, BR-signaling genes were downregulated in response to BPH. Thus, the growth of rice plants under BPH attack was reduced and defense related hormone signaling like JA, SA and ET were activated. In addition, growth-related hormone signaling pathways, such as GA, BR, and auxin signaling pathways, as well as ABA, were also found to be involved in BPH-induced defense. On the other side, 51 probe-sets (represented 50 genes) that most likely contribute to the impact of Bt rice on BPH were identified, including three early nodulin genes, four lipid metabolic genes, 14 stress response genes, three TF genes and genes with other functions. Two transcription factor genes, bHLH and MYB, together with lipid transfer protein genes LTPL65 and early nodulin gene ENOD93, are the most likely candidates for improving herbivore resistance in plants.