dataesr/scientific-paragraphs-categorization huggingface dataset:
For IFNγ intracellular staining, surface stained cells were incubated with Live/Dead fixable (Thermo Fisher Scientific) violet for 20 min in PBS to discriminate dead cells and subsequently fixed, permeabilized, and washed with BD Perm/Wash buffer (BD Biosciences) followed by incubation with IFNγ antibodies diluted in Perm/Wash buffer for 30 min at 4°C. Analysis was done on either LSR-2 or Fortessa X-20 flow cytometers equipped with a 350-nm laser (BD Biosciences). Subsequent data analysis was done using FlowJo software V10. 4 for Mac (Tree Star). High-content immunophenotyping analysis was performed at Centré d'Immunophénomique (see Fig. S4  C for gating strategies).
Final preamplified cDNA was diluted 1:5 in Tris-EDTA buffer. High-throughput gene expression analysis was performed using the 96. 96 dynamic arrays and Biomark HD system from Fluidigm in accordance with the manufacturer's instructions and standard settings. Obtained data were analyzed using the Real-Time PCR Analysis Software (Fluidigm), and resulting CT values were normalized to Cph to obtain dCT values. Heatmaps, Z-scores, and hierarchical clustering using the one minus Pearson correlation were generated using Morpheus (https://software. broadinstitute. org/morpheus/). PCA plots were generated using Qlucore Omics Explorer (Qlucore AB).
(E) Relative proportion of CD163 + TAMs among mTAMs in YUMM1. 7 tumors at the indicated time points; data are represented as mean ± SEM of n = 4. (F) Immunofluorescence staining for CD163 (green) and CD146 (indicating blood vessels, red) and nuclei (Hoechst, white) in a 200-µm-thick vibratome cross section of an entire tumor. Image was acquired as a tile-scan with Z-stacks using 10× objective and subsequent processed in 3D (left). High-resolution images was acquired as single Z-stack and processed in 3D (right). Scale bar, 1,000 µm. (G) High-throughput gene expression analysis of MN, iTAM, and mTAM subsets: (1) MHCII -CD163 -, (2) MHCII + CD163 -, (3) MHCII -CD163 lo , and (4) MHCII -CD163 hi (see Fig. S2D for full gating strategy). Z-scores were calculated by subtracting the ΔCT (sample) from the ΔCT (mean) across all samples.
(F) Gene expression analysis on tumor tissue at endpoint. Data are represented as mean ± SEM of n = 5. Statistically significant differences were calculated using a Kruskal-Wallis one-way ANOVA followed by Dunn's multiple comparisons test; *, P < 0. 05; **, P < 0. 01; ***, P < 0. 001. (G) Flow cytometric analysis of CD4 + , CD8 + , and iTAM infiltration in YUMM1. 7 tumor 1 d after αCD163-dxr treatment. Mice (n = 5 per time point) received one to five doses of 2 mg/kg dxr (red arrow indicates days of i. v. treatment). Total numbers of cells were calculated from the proportion of live cells and expressed per gram of tissue. (H) Mice inoculated with 10 6 YUMMER1. 7 cells on the right flank were randomized into groups and treated from day 10 with 2 mg/kg dxr or control every second day for 2 wk as previously described for YUMM1.
These studies were supported by the Novo Nordisk, L'Agence Nationale de la Recherche (grants ANR-09-MIEN-029-01 and ANR-10-BLAN-1302-01 to T. Lawrence), and the European Research Council (FP7/2007-2013 grant agreement number 260753 to T. Lawrence) and by institutional funding from the Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, and Aix-Marseille-Université. Microscopy facilities are supported by L'Agence Nationale de la Recherche (grant ANR-10-INBS-04-01 France Bio Imaging). S. K. Moestrup and J. H. Graversen are minority shareholders in the small biotech company Affincon Aps, which holds intellectual property rights to CD163 targeting. The remaining authors declare no competing financial interests. Author contributions: A. Etzerodt and T.
01. (F) Gene expression analysis on tumor tissue at endpoint. Data are represented as mean ± SEM of n = 5. Statistically significant differences were calculated using a Kruskal-Wallis one-way ANOVA followed by Dunn's multiple comparisons test; *, P < 0. 05; **, P < 0. 01; ***, P < 0. 001. (G) Flow cytometric analysis of CD4 + , CD8 + , and iTAM infiltration in YUMM1. 7 tumor 1 d after αCD163-dxr treatment. Mice (n = 5 per time point) received one to five doses of 2 mg/kg dxr (red arrow indicates days of i. v. treatment). Total numbers of cells were calculated from the proportion of live cells and expressed per gram of tissue. (H) Mice inoculated with 10 6 YUMMER1. 7 cells on the right flank were randomized into groups and treated from day 10 with 2 mg/kg dxr or control every second day for 2 wk as previously described for YUMM1.
III. WORKING POINT STABILITY STUDY Beam quality stability study involved tolerance analysis on pointing stability, driver-to-ionization pulses time jitter, pulses energies and background density fluctuations. Being QFluid a 2D code in cylindrical approximations for the fields, pointing stability analysis has been performed with the PIC code ALaDyn in 2D slab. Mechanical vibrations are expected to generate angular fluctuations in the pulses directions of μm rad scale. Having the driving train focusing parabola a focal length of about 2 m, the train of pulse is the most sensitive to pointing stability, while the ionization pulse transverse jitter at the interaction point is negligible. Electrons, therefore, at the leading order are extracted in the vicinity of the nominal axis but can experience transverse forces with a zero-axis placed at some distance (at most a few microns with our parameters) from the beam.
ACKNOWLEDGMENTS The research leading to these results has received funding from the  EU   Horizon 2020 Research and Innovation Program  under Grant Agreement No. 653782   EuPRAXIA. This project has received financial support from the  CNR funded Italian research Network ELI-Italy. We acknowledge the  S. Sinigardi  grant  IsB18_ALaRe  and the P. Londrillo grant IsC65_ENV-LWFA at CINECA under the  ISCRA initiative  for the HPC resources. The authors very much appreciate the support by  P. Londrillo  ( INAF, Italy ) and  M. Kirchen  ( DESY, Deutschland ) for their help with ALaDyn and FB-PIC codes.
Diagnosis We reviewed phenotypic (coloration, morphology) and genetic (genome size, karyotype, and sequence divergence) variation of the considered taxa. Coloration is illustrated by high-quality photographs of known geographic origins, taken by us and credited photographers. Besides detailing general characteristics, we compiled a dataset of snoutvent length (SVL) from published studies (Suppl. material 1, Table  S1 ), consisting of average SVL (computed separately for males and females) from 82 populations, totaling 6,004 individuals at least, and representing all taxa expect the narrowly distributed P. s. syriacus and P. b. chloeae (Suppl. material 1, Table  S1 ). We report the ranges (minimummaximum) and average values for each sex separately, and illustrate interpopulation variation by boxplots.
Results and discussion We updated the distributions and taxonomy of Eurasian spadefoot toads (genus Pelobates). Following recent molecular results  (Dufresnes et al. 2019b ), a total of eight extant clades are distinguished. Six of them correspond to species level divergences, given their confirmed or putative reproductive isolation, as inferred from hybrid zone analyses, which make ad hoc tests to evaluate where two lineages stand along the speciation continuum  (Singhal and Moritz 2013; Dufresnes et al. 2019b ). The remaining intraspecific lineages are accordingly treated as subspecies, because they featured extensive admixture and thus seem to lack reproductive barriers. From our SVL dataset, there was a significant global effect of species (P < 0. 001) but not of sex (P = 0. 08), neither of their interaction (P = 0.
material 1, Table  S1 ; Fig. 2 ). The karyotype includes six large and seven small pairs of two-armed chromosomes. Large centromeric C-bands appears in the pairs 1, 2, 4, 9, and 12; pericentric bands in the short arms of pair 1 and long arm of pair 8; telomeric bands in the long arms of pairs 1, 2, and 11; the short arm of pair 7 is almost heterochromatic  (Herrero and Talavera 1988). The nuclear DNA content averages 7. 3 pg  (Litvinchuk et al. 2013 ). As shown Between-population variation of average size (snout-vent length -SVL) for each Pelobates species, measured separately for females (pink) and males (blue). This compiles average size-data from 82 populations, representing at least 6,004 individuals (Suppl. material 1, Table  S1 ). For P. balcanicus, it only includes populations from the nominal P.
Acknowledgements We thank  M. Pajković  for translating publications from  Serbo-Croatian, P. Lymberakis (NHMC)  for processing the type series of P. b. chloeae,  A. Nöllert ,  G. Haimovitch , G. Martinez , and  A. Sanchez Vialas  for sharing their pictures,  P. -A. Crochet  for taxonomic advices and  Nicolas Perrin  for support. We are also grateful to  T. Vukov ,  O. Zinenko ,  A. Ohler ,  L. Ceriaco , and  A. Crottini , as well as an anonymous reviewer, for their expertise and useful feedback. M. D. is Research Director at  Fonds de la Recherche  scientifique (FNRS). This study was founded by a grant from the  Swiss National Science Foundation (SNSF)  to  Nicolas Perrin  (no. 31003A_166323 ), and a  SNSF  fellowship to CD (no. P2LAP3_171818 ).
Imaging and transcriptome analysis of single fission yeast cells We developed an integrated approach for imaging and isolation of single cells using a tetrad dissection microscope, followed by transcriptome analysis by scRNA-seq (Fig. 1a ). Datasets combining images and scRNA-seq libraries were generated for 2028 cells across a range of conditions, together with 780 matching control libraries, each obtained from 3 pg of total RNA (ctrRNA, Supplementary Table  1.
Applied to our datasets, bayNorm generated true count distributions highly similar to measurements from population RNA-seq (Supplementary Fig. 2b ) and to absolute mRNA counts obtained by smFISH (Supplementary Fig. 2c , Supplementary Table  5 )  17, 18. Finally, true counts correlated with expression levels derived from cell populations  18  , while preserving information about cell-to-cell variability in mRNA expression (average Rpearson=0. 78, Fig. 1c , Supplementary Fig. 2b-e ). In summary, we generated the first combined dataset of transcriptomes and microscopy images from single yeast cells, which is analysed in detail below.
Cell-to-cell variability of transcriptome regulation We first looked for mRNAs with high cell-to-cell variability compared to most genes (Highly Variable Genes, HVG). For this, we used 9 scRNA-seq datasets consisting of 864 unperturbed cells growing exponentially at 2-10x10  6  cell/ml (Supplementary Table  2 ). As reported previously and expected from stochastic gene expression models  19, 20  , coefficients of variation (σ/m, CV) and means of normalised counts were anti-correlated in cells and ctrRNA (Supplementary Fig. 3a ). We therefore defined HVGs as mRNAs with CVs significantly higher than this overall trend using simulated data with the Poisson noise as only source of variability as a reference (Methods, Fig  2a  and legend ). We applied this procedure to each dataset normalised separately because it led to CVs closest to smFISH measurements while avoiding batch effects (Supplementary Fig.
2c ). We identified 411 genes with CV significantly higher than the baseline in at least one dataset, of which 112 were also present in ctrRNAs and were discarded as false positives (FP). This analysis generated a list of 299 highconfidence HVGs (Supplementary Fig. 3b , Supplementary Table  6 ). To investigate the specificity of our scRNA-seq approach, we analysed 5 control genes and 8 HVGs covering a range of variability scores and biological functions by smFISH. HVGs showed significantly higher size-corrected Fano factors (scFano), confirming their higher noise levels (Fig  2b-c , Supplementary Fig. 3d ). Genes periodically expressed during the cell cycle have been identified in synchronised cell populations  [21] [22] [23] [24]. We hypothesised that these genes would be overrepresented among HVGs, as we sampled asynchronous cells from different cell-cycle stages.
Notably, most HVGs were not cell-cycle periodic and could not be identified in synchronised cell populations. To characterise these genes, we split them into three categories based on the number of datasets in which they were highly variable (>3: highly pervasive; 2-3: moderately pervasive; 1: lowly pervasive) (Fig. 2e , Supplementary Table  6 ). These categories describe how robustly variable each gene is across biological replicates. Importantly, some lowly pervasive genes, like lsd90, showed strong amplitudes of regulation (Fig. 2b , Supplementary Fig. 3c-d ). Moderately and highly pervasive HVGs were related to mitochondria and heat-shock response. Interestingly, the genes encoding Nmt1 and the associated biosynthetic enzyme Thi2 were among the most pervasively variable HVGs, suggesting widespread heterogeneity in vitamin B1 metabolism.
Cell size dependence of transcriptome regulation Single-cell RNA-seq provides a snapshot of gene-expression variability and cell states in a population. Interpreting this information can be greatly facilitated by integrating transcriptomics data with measures of quantitative cellular features  6, 19, [38] [39] [40]. Our combined approach offers such capabilities as it includes microscopy images of each cell matched to their respective transcriptomes (Fig. 1a ). We used cell-length measurements from images across all growth datasets to order cells as a function of size, which reflects progression through the cell cycle (Supplementary Table  3 ). We first examined changes in global properties of scRNA-seq measurements as a function of size. Mean cell length during rapid growth was 10. 9 µm, consistent with reported data (Fig.
3b , see below)  8, 42. We then used cell-length measurements to guide our analysis of transcriptional programmes associated with cell proliferation. To do so, we looked for genes differentially expressed between large cells in M/G1/S and small, recently born G2 cells using bayNorm priors specific for each groups (Fig. 3c , Supplementary Fig. 5b-c,e ). We identified 92 genes significantly up-regulated in large cells (Supplementary Table  6 ). Consistent with large cells being in M/G1/S, 28. 3% of these were also periodically expressed in synchronised cell populations  [21] [22] [23] [24]. Twice as many genes (193) were induced in small cells, of which 19. 2% are periodic  24. Importantly, this analysis combined with HVG detection based on ΔCV (Fig. 2 ) retrieved 81. 7% of the top-500 periodic genes present in the dataset, with the remaining genes showing no apparent regulation (Supplementary Fig.
05). Using k-means clustering, we defined 5 small gene clusters, 3 of which increased (Cl1-3) and 1 decreased (Cl5) in concentration (Fig. 3d , Supplementary Table  6 ). Cl4 showed significant, but low amplitude, positive regulation with size. We assessed whether these clusters defined one or more cellular states by looking at their correlations between single cells (Methods). We found evidence for two states in our datasets. The first was defined by Cl1 and Cl2 that were positively correlated and contained genes that are also up-regulated during meiotic differentiation (Fig. 3e )  47. Cl3 and Cl5 were anti-correlated and defined a second state containing a small number of genes functioning in carbohydrate metabolism (Fig. 3e ). Although not significant, this enrichment could hint at a gradual change in cellular energy metabolism coordinated with cell-size.
An orthogonal Random Forest approach confirmed that 69 of the 100 genes with the strongest non-linear correlation with cell size were either differentially expressed between big and small cells or escaped scaling (Supplementary Table  6 ). Together, this analysis uncovers gene expression programmes occurring during growth in G2 and escaping coordination with cell size. Interestingly, 45. 5% of HVGs from Fig. 2  were more highly expressed in large or small cells or escaped scaling. Their variability is therefore not stochastic but can be understood in the light of two physiological variables: cell size and cell-cycle stage. This demonstrates the potential of analysing scRNA-seq datasets in the context of quantitative cellular features to understand gene regulation.
All conditions are described in detail in Supplementary Table  2 , and assigned to individual samples in Supplementary Tables  1  and 3. Heat stress: cells were grown in YE medium at 25°C up to a density of 2-4 x 10 6 cells/ml. The cells were transferred to a water bath maintained at 37°C or 39°C (for datasets, 1712_1, 1712_2, 0408_2). For studying adaptation to heat, cells were transferred post-heat-shock to a turbidostat maintained at a density of 4 x 10 6 cells/ml and a temperature of either 25°C or 37°C (siphon-flow based derivative of the instrument described in ref  51  ). Glycerol growth: cells were grown in YE medium with 3% glycerol and 0. 1% glucose. Osmotic shock, Oxidative stress: Cells were cultured in YE medium up to a density of 4 x 10 6 cells/ml. To induce osmotic shock, an equal volume of 2M sorbitol prepared in YE medium was added to the cell culture to a final concentration of 1M for 15 min.
Data was processed using RTA 1. 18. 64, with default filter and quality settings. The reads were de-multiplexed with bcl2fastq-1. 8. 4 (CASAVA, allowing 0 mismatches). Read 1 was used to extract cell-specific indexes and Unique Molecular Identifiers (UMI). The corresponding Read 2 was mapped to the fission yeast genome as described in ref  18. Mapped reads where assigned to fission yeast genes as described in ref  18  using Pombase annotation as of 27/05/2015 and including 5' and 3' UTR sequences. Read1 and Read2 were assigned to specific cell/RNA samples based on cell-specific indexes sequences de-multiplexed using in house Perl scripts. Within each specific cell/RNA sample, reads sharing identical UMI sequences and mapping to the same gene were collapsed.
Our method is identical to the adjacency method introduced and implemented recently in UMI-tools  54. Applying our UMI error correction method removes ~ 30% of the raw reads pool (Supplementary Fig. 1b ). For dataset descriptions, statistics and raw counts see Supplementary Tables  1 2 3 4. Average gene (Supplementary Figure  1d ) Average profiles were obtained from raw UMI-corrected counts for 10 cells using the deeptools package  56. The function "computeMatrix scale-regions" and default bin size of 10nt and flanking regions of 300nt were used.
Selection of high-confidence genes Genes representing >0. 16% of the total molecules detected in the transcriptome of at least one cell across all datasets were included in the high-confidence gene-set used in this study. This empirical filter leads to a list of 1011 genes with varied functions and regulation patterns across conditions (Fig. 1b , Supplementary Fig. 1f-h , Supplementary Table  6 ). Importantly this approach included in the highconfidence list genes with high expression in a small number of cells. This would not have been possible using a single cut-off on mean expression values across the dataset. This filtering protocol removed mostly lowly expressed genes with very high fraction of cells with zero counts (dropouts) for which detection becomes mostly stochastic (Supplementary Fig.
Estimation of capture efficiencies of single cells Single cells' capture efficiencies (  ) are proportional to cell-specific global scaling factors (  ) that are commonly used in normalisation of single cell RNA-seq data (see for example  59  ): 𝛽𝛽 𝑖𝑖 = 〈𝛽𝛽〉 × 𝑠𝑠 𝑖𝑖 /〈𝑠𝑠〉 where the constant of proportionality is related to the mean capture efficiency 〈〉. The scaling factors (s i ) can be estimated using spike-in controls  57  , or alternatively from the data directly. Simple estimates of s i are the total number of molecules detected per cell (total count, TC), or the mean of the number of molecules of a subset of genes detected in each cell. Popular bulk RNA-seq methods such as DESeq are designed to compute global scaling factors s i 60,61. Unfortunately, these methods are not applicable to scRNA-seq datasets because of the high frequency of drop-outs present in the data (drop-outs: the proportion of genes with zero counts across cells)  59.
Alternative methods have been developed specifically for scRNA-seq (for example see  62, 63  ). We carefully assessed several existing methods for estimation of scaling factors, and settled for estimations of   based on the mean of UMI-corrected counts of a carefully chosen subset of genes in each given cell. The rationale behind this choice is the following: we argued that genes with i) high drop-out rates, ii) high variability in ctrRNA control experiments (showing technical variability), or, iii) those in the tail of the mean expression distribution (which have disproportionally high contribution to the total count) are not suitable for scaling factor estimation. Specifically, we used a list of 768 genes for global scaling factors estimation that were selected as follows: i) Genes with dropout rate > 70% were excluded (zero UMI-corrected counts in more than 70% of the cells across all datasets, 202 genes).
ii) The top 20 higher expressed genes across datasets after TC normalisation were removed. iii) Genes with significantly high technical variability in ctrRNA controls were removed. To do this, we called highly variable genes in 11 ctrRNA datasets using the Bioconductor package scran and excluded 21 genes that were noisy in at least 5 of the 11 datasets  63, 64. Interestingly, the procedure described above produced the highest correlation between single-cell capture efficiencies and cell sizes (0. 1781 for this procedure, 0. 149 for the method proposed in  63  and 0. 0568 for the spike-in estimates).
Using the Bayes rule, the posterior distribution of the  ℎ gene in the  ℎ cell can be expressed as: Outputs of the bayNorm normalisation procedure are either samples from the posterior distribution described above or its maximum a posteriori estimate (Supplementary Fig. 2a ). In addition to raw RNA-seq counts, bayNorm normalisation requires as inputs prior distributions of the parameters   and   for each gene. In bayNorm, these are estimated from the scRNA-seq data directly using an Empirical Bayes approach (see ref  17  for details). Prior estimation can be done using data from all cells across all datasets irrespective of experimental conditions. We refer to this procedure as "Global" normalisation. Pr(𝑥𝑥 𝑖𝑖𝑖𝑖 0 |𝑥𝑥 𝑖𝑖𝑖𝑖 , Alternatively, if cells can be split in different groups based on experimental conditions or phenotypic information for instance, prior parameters   and   can also be estimated within each group independently.
27% to 3. 56% in the cell datasets for high-confidence genes). Second, bayNorm effectively corrects for the experimental batch dependent variation in average capture efficiency and reduces batch-specific biases performing similarly to SCnorm but without the need for multiple expression-dependent scaling factors  17, 62. Also, use of global priors as explained above can further reduce batch effects. Third, bayNorm normalisation preserves the uncertainty present in the data particularly for cells with low coverage, reducing false discovery rates in differential expression analysis. Finally, bayNorm produces mRNA distributions and noise estimates close to the state-of-the-art smFISH measurements (Supplementary Fig. 2c , Supplementary Fig. 3c-d ) and averaged transcriptome structures close to high-quality population estimates (Fig.