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1. A florigen-expressing subpopulation of companion cells expresses other small proteins and reveals a nitrogen-sensitive FT repressor

   https://doi.org/10.1101/2024.08.17.608367  

   Takagi, Hiroshi; Ito, Shogo; Shim, Jae Sung; Kubota, Akane; Hempton, Andrew K; Lee, Nayoung; Suzuki, Takamasa; Yang, Chansie; Nolan, Christine T; Bubb, Kerry L; et al (August 2024, bioRxiv)

   Abstract The precise onset of flowering is crucial to ensure successful plant reproduction. The geneFLOWERING LOCUS T(FT) encodes florigen, a mobile signal produced in leaves that initiates flowering at the shoot apical meristem. In response to seasonal changes,FTis induced in phloem companion cells located in distal leaf regions. Thus far, a detailed molecular characterization of theFT-expressing cells has been lacking. Here, we used bulk nuclei RNA-seq and single nuclei RNA (snRNA)-seq to investigate gene expression inFT-expressing cells and other phloem companion cells. Our bulk nuclei RNA-seq demonstrated thatFT-expressing cells in cotyledons and in true leaves differed transcriptionally. Within the true leaves, our snRNA-seq analysis revealed that companion cells with highFTexpression form a unique cluster in which many genes involved in ATP biosynthesis are highly upregulated. The cluster also expresses other genes encoding small proteins, including the flowering and stem growth inducer FPF1-LIKE PROTEIN 1 (FLP1) and the anti-florigen BROTHER OF FT AND TFL1 (BFT). In addition, we found that the promoters ofFTand the genes co-expressed withFTin the cluster were enriched for the consensus binding motifs of NITRATE-INDUCIBLE GARP-TYPE TRANSCRIPTIONAL REPRESSOR 1 (NIGT1). Overexpression of the paralogousNIGT1.2andNIGT1.4repressedFTexpression and significantly delayed flowering under nitrogen-rich conditions, consistent with NIGT1s acting as nitrogen-dependentFTrepressors. Taken together, our results demonstrate that majorFT-expressing cells show a distinct expression profile that suggests that these cells may produce multiple systemic signals to regulate plant growth and development. 

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2. Florigen-producing cells express FPF1-LIKE PROTEIN 1 that accelerates flowering and stem growth in long days with sunlight red/far-red ratio in Arabidopsis

   https://doi.org/10.1101/2024.04.26.591289  

   Takagi, Hiroshi; Lee, Nayoung; Hempton, Andrew K; Purushwani, Savita; Notaguchi, Michitaka; Yamauchi, Kota; Shirai, Kazumasa; Kawakatsu, Yaichi; Uehara, Susumu; Albers, William G; et al (April 2024, bioRxiv)

   Summary Seasonal changes in spring induce flowering by expressing the florigen, FLOWERING LOCUS T (FT), inArabidopsis.FTis expressed in unique phloem companion cells with unknown characteristics. The question of which genes are co-expressed withFTand whether they have roles in flowering remains elusive. Through tissue-specific translatome analysis, we discovered that under long-day conditions with the natural sunlight red/far-red ratio, theFT-producing cells express a gene encoding FPF1-LIKE PROTEIN 1 (FLP1). The masterFTregulator, CONSTANS (CO), controlsFLP1expression, suggestingFLP1’s involvement in the photoperiod pathway. FLP1 promotes early flowering independently ofFT,is active in the shoot apical meristem, and induces the expression ofSEPALLATA 3(SEP3), a key E-class homeotic gene. Unlike FT, FLP1 facilitates inflorescence stem elongation. Our cumulative evidence indicates that FLP1 may act as a mobile signal. Thus, FLP1 orchestrates floral initiation together with FT and promotes inflorescence stem elongation during reproductive transitions. 

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3. Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

   https://doi.org/10.1038/s41598-020-67513-5  

   Alvarez, Marcus; Rahmani, Elior; Jew, Brandon; Garske, Kristina M.; Miao, Zong; Benhammou, Jihane N.; Ye, Chun Jimmie; Pisegna, Joseph R.; Pietiläinen, Kirsi H.; Halperin, Eran; et al (December 2020, Scientific Reports)

   null (Ed.)

   Abstract Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem . 

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4. netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis

   Elyanow, Rebecca; Dumitrascu, Bianca; Engelhardt, Barbara E; Raphael, Benjamin J. (January 2019, Research in Computational Biology (RECOMB))

   Single-cell RNA-sequencing (scRNA-seq) enables high throughput measurement of RNA expression in individual cells. Due to technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard analysis methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc combines network-regularized non-negative matrix factorization with a procedure for handling zero inflation in transcript count matrices. The matrix factorization results in a low-dimensional representation of the transcript count matrix, which imputes gene abundance for both zero and non-zero entries and can be used to cluster cells. The network regularization leverages prior knowledge of gene-gene interactions, encouraging pairs of genes with known interactions to be close in the low-dimensional representation. We show that netNMF-sc outperforms existing methods on simulated and real scRNA-seq data, with increasing advantage at higher dropout rates (e.g. above 60%). Furthermore, we show that the results from netNMF-sc -- including estimation of gene-gene covariance -- are robust to choice of network, with more representative networks leading to greater performance gains. 

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5. Gene representation in scRNA-seq is correlated with common motifs at the 3′ end of transcripts

   https://doi.org/10.3389/fbinf.2023.1120290  

   Li, Xinling; Gibson, Greg; Qiu, Peng (May 2023, Frontiers in Bioinformatics)

   One important characteristic of single-cell RNA sequencing (scRNA-seq) data is its high sparsity, where the gene-cell count data matrix contains high proportion of zeros. The sparsity has motivated widespread discussions on dropouts and missing data, as well as imputation algorithms of scRNA-seq analysis. Here, we aim to investigate whether there exist genes that are more prone to be under-detected in scRNA-seq, and if yes, what commonalities those genes may share. From public data sources, we gathered paired bulk RNA-seq and scRNA-seq data from 53 human samples, which were generated in diverse biological contexts. We derived pseudo-bulk gene expression by averaging the scRNA-seq data across cells. Comparisons of the paired bulk and pseudo-bulk gene expression profiles revealed that there indeed exists a collection of genes that are frequently under-detected in scRNA-seq compared to bulk RNA-seq. This result was robust to randomization when unpaired bulk and pseudo-bulk gene expression profiles were compared. We performed motif search to the last 350 bp of the identified genes, and observed an enrichment of poly(T) motif. The poly(T) motif toward the tails of those genes may be able to form hairpin structures with the poly(A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during scRNA-seq library preparation, which is a mechanistic conjecture of why certain genes may be more prone to be under-detected in scRNA-seq. 

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