dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure determination A BLAST search (https://blast. ncbi. nlm. nih. gov/) identified the fluorescent protein phiYFPv from the jellyfish genus Phialidium as the closest homolog of both AausFP1 and AausFP2 (sequence identities of 61% and 50%, respectively) with a known structure (PDB entry code 4HE4  [43] ). The structures of AausFP1 and AausFP2 were solved by the molecular replacement method using the 4HE4 coordinates as a search model with the program PHASER  [44]. The model was progressively and interactively modified in COOT  [45]  and refined with REFMAC5  [46]. The asymmetrical units contain 4 molecules for AausFP1 and 1 molecule for AausFP2. Analysis of the interaction interfaces with PISA  [47]  strongly suggests that the AausFP1 tetramer consists of a dimer of a physiological dimer (interface areas of 1,210 Å 2 versus 360 Å 2 for the third and fourth largest areas), while the AausFP2 monomer forms a physiological dimer with a symmetry-related molecule (interface area of 1,290 Å 2 versus 540 Å 2 for the second largest area).
Acknowledgments We dedicate this manuscript to the memory of  Dr. Roger Y. Tsien  and  Dr. Osamu Shimomura , whose studies on A. victoria and avGFP continue to inspire us and to catalyze new technologies for biological imaging. This work was also made possible by the  Crystal Jelly exhibit at the Birch Aquarium at Scripps , highlighting the significance of this species in the history of biomedical research. This exhibit was the source of the A. victoria individual used in this work. The  European Synchrotron Radiation Facility  is acknowledged for access to beamline ID30B and facilities for molecular biology via its in-house research program. The  ALBA synchrotron  is acknowledged for allocation of beamtime on beamline BL13-XALOC. We thank  Franck Borel ,  David Cobessi , and the beamline staff for help during data collection on BL13-XALOC.
Our routine identifies transcriptionally active regions (TARs) using a hidden Markov model, generates a matrix of expression levels for all TARs across all cells in a dataset, performs single-cell TAR expression analysis to identify TARs that are biologically significant, and then annotates biologically significant TARs using gene homology analysis. This procedure leverages single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts in complex tissues and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.
INTRODUCTION: Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study development, tissue, and disease biology  [1] [2] [3] [4] [5] [6] [7] [8] [9]. scRNA-seq analyses currently rely on the availability of high quality genome annotations to define cell features and to perform cell clustering, dimensionality reduction, differential gene expression, and other analyses  [10] [11] [12] [13] [14]. Identifying genes and correctly annotating their locations and coding regions within the genome is a difficult task that collaborative projects such as Ensembl, ENCODE, and GENCODE are striving to achieve. Gene annotations are generated using homology analyses, updated based on experimental evidence, and manuallycurated for select organisms such as human, mouse, and zebrafish  15. For less routinely studied organisms, fewer RNA-seq datasets are available and gene annotations are dependent on purely computational approaches such as phylogenetic sequence comparison with minimal verification.
Consequently, as we show here with scRNA-seq experiments and analyses spanning six species (human, mouse, chicken, gray mouse lemur, naked mole rat, and sea urchin), significant number of genes and cell type specific information is missed in current scRNA-seq workflows. To overcome this hurdle, we propose a procedure to perform scRNA-seq data analysis in the absence of a high-quality gene annotation (Fig. 1A ). This procedure first identifies transcriptionally active regions (TARs) using a hidden Markov model (HMM)  16. These HMMderived TARs capture the transcriptional activity in the sample, limited only by the scRNA-seq measurement and quality of the genome assembly. Single-cell TAR expression analyses are then performed, using both annotated TARs (aTARs) and unannotated TARs (uTARs), revealing the degree of biologically relevant information that exists both inside and outside of known gene annotations.
Gene annotation independent scRNA-seq analysis We developed a gene-annotation-independent scRNA-seq analysis and benchmarked it using publicly available and new datasets generated on the 10X Genomics platform (Methods). We explored different genome assemblies and annotations, including recent assemblies for human, mouse, chicken, gray mouse lemur, naked mole rat, and sea urchin, as well as prior annotations for human (Figs. 1B-D ). The size of the genome assemblies ranged from ~1 billion base pairs to ~3 billion base pairs (Fig. 1B ). As a proxy for the completeness of different gene annotations, we compared the number of annotated transcripts relative to the size of the genome assembly (Fig. 1C ). We found that less routinely studied organisms such as the naked mole rat and sea urchin, as well as an older human genome annotation (RefSeq hg16 annotations for the hg16 reference assembly) have a lower number of annotated transcripts relative to the genome assembly size, as expected.
We next quantified the number of sequences that align outside the gene annotation (assigned to uTARs, see below). For a recent human gene annotation (GENCODE v30 hg38), only 2% of reads aligned outside of the annotation. Considerably less effort has been devoted to the genome annotations of less routinely studied species, as is evident from the percentage of sequences that fall outside of the most current genome assemblies for gray mouse lemur (7. 1%), nake mole rat (4. 2%), and purple sea urchin (10. 7%) (Fig. 1D ). We examined older human genome assembly releases and found a decrease in the percentage of reads that mapped outside of annotations, as annotations improved from 2004 to 2019 (Fig. 1E ). We sought to perform scRNA-seq analysis based on all transcriptional products detected by the assay, including those that are not included in annotations.
To identify TARs, we implemented a HMM after alignment of the RNA-seq data to the reference genome (Methods)  16. Fig. 1F  shows an example of TARs identified for human genome build 16 along a segment of chromosome 22 comprising IGLL5. Through comparison to the genome annotation, we labeled TARs as either annotated (aTARs) or unannotated (uTARs). Finally, we counted the number of transcripts for each TAR in each cell in a dataset, creating a digital expression matrix.
______________________________________________________________________ We used Seurat to analyze single-cell gene and TAR expression profiles  17, 18. We performed dimensionality reduction and single-cell clustering analysis on both TAR and gene expression profiles to determine the extent of biologically significant information contained within uTARs. We analyzed the Tabula Muris 7 10X generated dataset for the mouse spleen and kidney, human PBMC data available through 10X Genomics  19  , a mouse lemur lung dataset, a publicly available naked mole rat spleen dataset  20  , and a publicly available sea urchin dataset  21. In addition, we performed single-cell RNA-seq profiling of embryonic chicken heart tissue at different stages of development (days 4, 7, 10, and 14 post fertilization). Following data pre-processing (Methods), we implemented dimensionality reduction (principal component analysis) and used UMAP  22, 23  to visualize cells in 2D.
We labeled cell groups based on existing metadata or expression of canonical marker genes (Methods). We found a significant number of mm10 assembly bases annotated by uTAR features (Extended Data Fig. 1A ). Comparison of UMAP visualizations of single-cell gene and uTAR expression profiles revealed that significant cell type dependent structure is retained in the uTAR UMAPs (Fig. 2A , Extended Data Fig. 1B ). We computed silhouette values as a measure of the consistency within clusters of the data and found close agreement between silhouette values calculated for aTAR and gene expression-based clustering, as expected, but also good agreement between uTAR and gene expression based clustering for several datasets (Fig. 2B ). This observation suggests that scRNA-seq reads outside of gene annotations contain significant biological information that can accurately separate cell types.
Dimensional reduction on human PBMC uTAR expression reveals significant structure for the older hg16 genome build, but not for the most recent hg38 build, as expected given that hg16 is older and has less comprehensive annotation (Extended Data Fig. 1B ). Dimensional reduction on mouse spleen uTAR expression reveals structure in many cell types such as T and B cells while dimensional reduction on mouse kidney uTARs reveals less structure. For mouse lemur lung, naked mole rat spleen, and sea urchin embryo, we find that dimensional reduction on single-cell uTAR expression profiles reveals near identical structures to that of annotated gene expression profiles. We conclude that for species that are not routinely studied and for which high quality gene annotations are not available, extensive biologically significant information is missed by conventional scRNA-seq analysis.
2C , correlation r 2 =4. 0e-3, p-value < 2. 2e-  16 ). This analysis indicates that uTARs with high total expression are not necessarily those that best define cell types, demonstrating the utility of scRNA-seq analysis as a selection filter before annotation and validation. We next used the Wilcox Rank Sum test to identify uTARs differentially expressed in the naked mole rat spleen dataset (Methods). We identified the largest peak within each differentially expressed uTAR and identified homologous sequences to the peak sequence using BLAST. This analysis revealed that uTARs that are more differentially expressed in any cell type (i. e. higher average log-e fold change expression relative to all other cell types) are also more likely to have sequence homology to transcripts contained within the NCBI nucleotide collection database, many of which have high homology to mRNA transcripts (Fig.
We explored uTARs that were differentially expressed between immune cell types within the spleen from the Tabula Muris dataset (Fig. 3A ). We uncovered a uTAR differentially expressed in natural killer cells containing reads with high sequence homology to GTH1 which plays a role in protecting cells from reactive oxygen species  26. We also found a uTAR containing homology to PRPF8, differentially expressed in macrophages, which is a pre-mRNA splicing factor that is essential for the catalytic step II in pre-mRNA splicing  27. In addition, we found uTARs containing homology to SNX29 and ATF7IP2, which were both upregulated in a small population of dendritic cells in the mouse spleen. SNX29 is broadly associated with microtubule motor activity and phosphatidylinositol binding and ATF7IP2is a transcription factor that couples other transcription factors to the transcription machinery.
Both genes have predicted mouse orthologs but are unannotated in the GENCODE vM21 genome annotation. We explored whether differentially expressed uTARs found in early stages of embryonic heart development were also differentially expressed in later stages. Silhouette coefficient analysis of the chicken dataset shows that uTARs better separate cell types in early development (Fig. 2A, B , Extended Data Fig. 1B ). We identified 18 differentially expressed uTARs in the day 4 chicken heart with high differential expression and labeled them based on sequence homology (Fig. 3B  dotplot). These include CLEC14A, a potential tumor endothelial marker that plays a role in angiogenesis  28  , SOX5, a transcription factor that regulates embryonic development and cell fate differentiation  29, 30  , and RUNX1T1, a transcriptional co-repressor that suppresses oncogenesis  31, 32.
While SOX5 and RUNX1T1 are both annotated elsewhere in the chicken genome, we found uTARs with high sequence homology to these genes. The end of the SOX5 uTAR is ~120kb upstream of the SOX5 annotation, which is ~340kb in length, and the end of the RUNX1T1 uTAR is ~50kb upstream of the annotated counterpart, which is ~87kb in length. This suggests that the SOX5 and RUNX1T1 uTARs may be additional exons of the existing annotations. When we examined the same set of differentially expressed uTARs in day 14 chicken heart, we found that most were not differentially expressed (Extended Data Fig. 1C ) suggesting a role for these differentially expressed uTARs in early development. Additionally, some differentially expressed uTARs in day 4 also had lower total read coverage in the day 14 dataset such as RUNX1T1 and CLEC14A (Extended Data Fig.
1C  coverage tracks). Thus, uTAR analysis can identify potentially novel development stage specific transcripts. We also explored differentially expressed uTARs in the gray mouse lemur lung, naked mole rat spleen, and sea urchin embryo datasets. Several differentially expressed uTARs were found in the mouse lemur EPCAM+ lung cells including H3F3C and BST2 (Fig. 3C ). H3F3C is a novel unannotated gene that plays a role in maintaining nucleosome structure  33, 34  while BST2 is associated with interferon gamma and other cytokine signaling pathways in the immune system  35, 36. Differentially expressed naked mole rat uTARs were also identified within neutrophils, T cells, and macrophages (Fig. 3D ). Homology analysis revealed these included DUSP1, NATD1, and TRG. The DUSP1 uTAR is a novel feature missing from the HetGla_1.
Spatial transcriptomics reveals spatial co-expression of uTARs and canonical gene markers. We used data generated on the 10X Genomics Visium spatial transcriptomics platform to visualize the spatial co-expression of differentially expressed uTARs with canonical gene markers. 10 µm thick coronal tissue slices of embryonic heart at days 4 and 14 post fertilization were used for spatial transcriptomic analysis. The resulting data comprised 747 and 1995 barcoded spots for day 4 (5 hearts) and day 14 (1 heart) respectively. We found that the expression of a SH3BGRrelated uTAR co-localized with that of the canonical cardiomyocyte marker TNNT2 at both day 4 and day 14 (Fig. 4A  left), in line with observations from scRNA-seq (Fig. 3B , Extended Data Fig. 1C ). We also observed clear expression of RUNX1T1 uTAR in the day 4 tissue section but no expression in the day 14 section, in concordance with the scRNA-seq data that suggested stagedependent expression of this uTAR (Fig.
4A right ). Expression of the RUNX1T1 uTAR at day 4 colocalized with a subset of COL1A1+ spots which is a marker for epicardial cells. Interestingly, the annotated RUNX1T1 gene has almost no expression in the spatial data revealing a discrepancy between annotated gene expression and uTAR expression. We quantified the correlation between the spatial expression of 11 uTARs in the day 4 sample and several canonical gene markers such as HBZ for red blood cells, TNNT2 for myocytes, and COL1A1 for epicardial progenitors (Pearson correlation, log-e normalized spot expression). We then performed hierarchical clustering on correlation values (Fig. 4B ). We found that spatial uTAR expression in several cell types colocalized with canonical markers for those cells. Immune and immature fibroblast cell types are two of the 3 least abundant cell types according to the scRNA-seq data.
DISCUSSION: Current scRNA-seq analysis relies on the availability of high-quality gene annotation to identify cell types, study dynamic transcriptional behavior over time, and predict gene interactions. We developed a method to identify unannotated TARs that distinguish cell types in single-cell transcriptomic data. This approach is most relevant for the analysis of scRNA-seq datasets from less routinely studied organisms, and datasets from developing tissues that often comprise, as we show here, transcriptional products that are not represented in gene annotations. We present proofof principle applications of this procedure to study unannotated transcripts in embryonic development and to explore previously unknown genomic features in several species. The strategy we propose broadly identifies transcriptionally active regions, without attempting to filter, curate, or characterize those regions, and then employs single-cell expression analyses to identify regions outside of the gene annotation that are relevant for the identification of cell states or types.
METHODS: Publicly available datasets: Fastq files for the human PBMC dataset were downloaded directly from the 10X Genomics library of single cell gene expression data. Tabula Muris alignment files (BAMs) for 10X Genomics droplet generated data were downloaded from the Gene Expression Omnibus (GSE109774) and fastq files were extracted using the 10X Genomics bamtofastq tool 39. Mouse uTARs were generated based on combining alignment files across all droplet generated data and scRNA-seq analysis for kidney (10X_P4_5) and spleen (10X_P4_6) samples are shown. Fastq files for the naked mole rat and sea urchin datasets were downloaded from GEO listed in their respective publications (GSM3885302 and GSE134350 respectively). Naked mole rat uTARs were generated based on combining alignment files for the spleen samples (SRR9291380, SRR9291381, SRR9291382, SRR9291383, SRR9291384, SRR9291385, SRR9291386, SRR9291387) and scRNA-seq analysis for sample SRR9291380 (nmr_1.
1) is shown. Sea urchin embryo uTARs were generate based on combining alignment files generated from SRR9693264, SRR9693265, and SRR9693266 and scRNA-seq analysis for sample SRR9693264 (D1) is shown. Mouse lemur lung tissue uTARs were generated from the Tabula Microcebus consortium by combining MLCA_ANTOINE_LUNG_EPCAM_POS_S12, MLCA_ANTOINE_LUNG_CD31_POS_S11, MLCA_ANTOINE_LUNG_P3_S7 datasets and scRNA-seq analysis for MLCA_ANTOINE_LUNG_EPCAM_POS_S12 is shown. Generation of chicken embryonic heart scRNA-seq data: Fertile white leghorn chicken eggs were incubated using an egg incubator that controls humidity and temperature until the embryonic day of interest. Chicken embryonic ventricular free walls were excised aseptically in ice cold Hank's Balanced Salt Solution (HBSS) and then minced into one-millimeter tissue fragments.
The 10X Genomics gene expression Library v3 Kit was used to isolate 4DPF and 7DPF samples. Illumina NextSeq500 paired-end sequencing was used to sequence each sample. Chicken uTARs were generated by combining sequence alignment files for all samples. scRNA-seq analysis shown for the right ventricle of day 7, 10, and 14. Sequence alignment and generation of expression matrices: Fastq sequence files were processed (adapter trimming, barcode tagging) using the Drop-seq  40  suite of computational tools and were aligned to respective genomes using STAR without gene annotation indexing. Gene expression matrices were generated based on gene annotations and TAR expression matrices were generated based on TAR annotations (described below) using Drop-seq tools. Identifying TARs from alignment files using groHMM: We used groHMM  16  to predict transcribed regions from aligned scRNA-seq data in a strand specific manner without annotations.
Uniquely mapped reads from multiple scRNA-seq data were merged (i. e. without consideration for coverage of individual cells). We down sampled the combined alignment file of the Tabula Muris dataset and kept 5% of all uniquely aligned reads to account for the computational memory constraints of the groHMM tool. Then, we used groHMM to scan the mapped read counts along the genome with window size 50bp without overlap. The emission probabilities were modeled by a gamma distribution. The gamma distribution parameters and transition probabilities were learned using the Baum-Welch expectation maximization (EM) algorithm. We predicted the transcribed regions from both the sense and antisense strands. Predicted transcribed regions within 500bp were merged using bedtools 41 merge (parameters: -s -d 500).
The coverage of each region was calculated using bedtools coverage (parameters: -s -counts -split). Transcribed regions with at least n reads were kept and used as TAR features in the following analysis, where n is set as 1/10,000,000 of uniquely mapped reads in the combined alignment file derived from samtools 42 view (parameters: -q 255). TARs identified using the groHMM algorithm were labelled as annotated TAR (aTAR) or unannotated TAR (uTAR) features based on their overlap with existing gene annotations. The refFlat genome annotation format was used in all cases where the genomic start and end position of each transcript is recorded. HMM features that have any overlap with an existing gene annotation transcript based strictly on transcription start and stop sites were labelled as "aTAR", without accounting for strandedness.
Single-cell transcriptome analysis: We used the Seurat suite of scRNA-seq tools  17, 18  to analyze gene expression and TAR expression data. Cells were filtered based on the number of gene expression features and TAR features were filtered based on being expressed in at least 2 cells. Cell filtering parameters were different for each dataset to optimize downstream analysis and to account for different protocols. Tabula Muris cells were filtered and labeled based on metadata information provided in the paper. Human PBMC cells were filtered based on having between 201-2599 gene features and less than 5% mitochondrial transcripts according to the hg38 assembly and GENCODEv30 annotations. Chicken cells were filtered based on having more than 200 gene features and less than 20% mitochondrial transcripts.
Gray mouse lemur cells were filtered based on having between 201-2499 gene features. Naked mole rat cells were filtered based on having between 401-2499 gene features. Sea urchin cells were filtered based on having between 401-2499 gene features. Gene and TAR combined expression matrices were log-normalized and scaled before further processing. For the human PBMC dataset, 4000 variable features (including genes and TARs) were found based on mean-variance ratios. PCA was performed on variable gene features, aTARs, and uTARs in the human PBMC dataset. PCA was performed using all genes, aTARs, and uTARs in the other datasets. Cells were clustered using a KNN graph-based clustering approach with the first 10 PCs derived from gene expression reduction. UMAP projections were derived from the first 10 PCs using either gene expression, aTAR expression, or uTAR expression.
Identifying differentially expressed uTARs and labelling uTAR: We used the Wilcoxon Rank Sum test to identify differentially expressed genes and TARs in every cell cluster, filtered based on increased expression as compared to all other cells. Canonical marker gene analysis was performed to identify cell clusters in days 4 and 14 chicken heart, naked mole rat spleen, and gray mouse lemur lung datasets. Differential mouse spleen uTARs were filtered by being in at least 25% of cells in the cluster and more than 0. 5 log-e fold change compared to the rest of the cells. Differential day 4 chicken uTARs were further filtered by being in at least 25% of cells in the cluster and more than 0. 25 log-e fold change compared to the rest of the cells. Lemur lung differential uTARs were further filtered based on being in at least 50% of cells in the cluster and more than 0.