dataesr/scientific-paragraphs-categorization huggingface dataset:
Reverse Transcription (RT) and Quantitative Polymerase Chain Reaction (qPCR) Total RNA of cultured cells was extracted using Tri-Reagent (Sigma) and digested with DNase (Thermo Fisher Scientific). Total RNA (800 ng) was reverse-transcribed for cDNA synthesis using Maxima Reverse Transcriptase kit (Thermo Fisher Scientific) following the manufacturer's instructions, and qPCR was performed using the SsoAdvanced Universal SYBR Green Supermix (BioRad) on a CF×96 real time system device (BioRad) according to the manufacturer's instructions. All samples were analyzed using the CFX Manager Software (BioRad). The mRNA levels were normalized to GAPDH. Primers for qPCR were the following: pri-miR-155 Forward 5 -GGGAGGATGACAAAGAAGCA-3 , Reverse 5 -TGAACATCC CAGTGACCAGA-3 ; miPEP155 Forward 5 -TGGAGATGGCTCTAATGGTG-3 , Reverse 5 -AACCACAGATT TCCCCTTCC-3 ; pri-miR-497 Forward 5 -TCTGACTGGGAGTGGAGGAAC-3 , Reverse 5 -CACATTTGG GGTGCAGGAGAA-3 ; miPEP497 Forward 5 -ATGGGCTGGGACGGGTTT-3 , Reverse 5 -AGGGGTTCCTC CACTCCC-3 ; GAPDH Forward 5 -GAAGGTGAAGGTCGGAGTCA-3 , Reverse 5 -GAAGATGGTGAT GGGATTTC-3.
Dual Luciferase Reporter Assays Hela or PC3 cells were seeded in 12-well plates to reach 70% confluence the day of transfection, and then transfected with the luciferase sensors of miRNA activity (psiCHECK2) (25 ng), with either the miRNA control or the specific pre-miR miRNA precursors (0. 1, 1 or 10 nM), or the empty vector or miPEP expressing vectors (250 ng). Transfected cells were lysed 48h after transfection, and luciferase activities were assayed by a Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer's instructions, with a Luminoskan and Skanlt Tm software for microplate readers (Thermo Scientific). The data were analyzed by normalizing Renilla luciferase activity (which quantifies the miRNA activity) with Firely luciferase activity (to monitor the transfection efficiency) and the ratio of Renilla luciferase activity to Firefly luciferase activity was calculated to indicate the activity of the reporter.
Data Availability Statement: The data presented in this study are openly available in http:// gwips. ucc. ie (accessed on 4 March 2021) at doi:10. 1093/nar/gkt1035, reference number  [17] ; in www. openprot. org (accessed on 4 March 2021) at doi:10. 1093/nar/gky936, reference number  [28].
EGR2 expression by alveolar macrophages is dependent on TGFb and GM-CSF Alveolar macrophages derive from foetal monocytes that seed the developing lung in the late gestational period  (7). To determine the point at which EGR2 is first expressed, we assessed EGR2 expression by E10. 5 yolk sac macrophages, by macrophages in the embryonic lung (E16. 5) and by CD11c hi CD11b lo alveolar macrophages in the neonatal and adult lung using the ImmGen database. This revealed that Egr2 was absent from yolk sac macrophages and macrophages in the embryonic lung at E16. 5, but it was expressed by both neonatal and adult alveolar macrophages (Figure  5A ), suggesting that it is induced during alveolarization in the neonatal period. Consistent with this, we found high expression of EGR2 at protein level by neonatal (d1) CD64 + lung macrophages (sometimes referred to as 'pre-alveolar macrophages') in Egr2 fl/fl mice; as expected, this expression was deleted efficiently in Lyz2 Cre/+.
This revealed that GM-CSF was also capable of driving EGR2 expression in this system (Figure  5I ). Given that GM-CSF receptor and TGF-bR signalling is known to induce expression of PPAR-g  (8, 9, 14) , we next determined if PPAR-g is upstream of EGR2. Analysis of a publicly available dataset (ImmGen) comparing the transcriptional profile of Pparg-sufficient and -deficient alveolar macrophages revealed downregulation (2. 1-fold change) of Egr2 in the context of Pparg deficiency (Figure  5j ). In contrast, Pparg expression was unaffected in alveolar macrophages from Lyz2 Cre/+. Egr2 fl/fl mice (Figure  5K ), suggesting EGR2 is downstream of PPAR-g. Another transcription factor implicated in controlling alveolar macrophage differentiation is C/EBPb  (15)  and EGR2 has been shown to modulate C/EBPb in vitro  (36).
Samples were acquired using a FACS LSRFortessa or AriaII using FACSDiva software (BD) and analyzed with FlowJo software (version 9 or 10; Tree Star). Analysis was performed on single live cells determined using forward scatter height (FCS-H) versus area (FSC-A) and negativity for viability dyes. mRNA was detected by flow cytometry using # This manuscript has been accepted for publication in Science Immunology. This version has not undergone final editing. Please refer to the complete version of record at www. scienceimmunology. org. The manuscript may not be reproduced or used in any manner that does not fall within the fair use provisions of the Copyright Act without the prior, written permission of AAAS.
BAL fluid analysis. The first BAL wash was centrifuged at 400g for 5mins and supernatant removed and Data and materials availability. All data needed to evaluate the conclusions in the paper are present in the paper or the Supplementary Materials, and RNA-seq data have been deposited in National Center for Biotechnology Information Gene Expression Omnibus public database (www. ncbi. nlm. nih. gov/geo/). Population-level RNA-seq (accession code: GSE182044) and scRNA-seq (accession code: GSE181894). Fcgr1 iCre mice are available from Prof. Bernard Malissen under a material transfer agreement with the Centre d'Immunologie de Marseille-Luminy, Aix Marseille Université. Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Calum Bain (calum.
9 nm over the visible spectrum. Spectral images were then unmixed with Zen software (Carl Zeiss) using reference spectra acquired from unstained tissues (tissue autofluorescence) or beads labelled with single fluorophores. 4D images analysis. Timelapse images analysis and visualization was performed using Imaris (Bitplane). Neutrophils and macrophages were segmented and tracked using the 'surface' tool using Supplementary Figure 11.
Epcam, Spp1 and Cd63 from the RNA-seq dataset (left panels), representative flow cytometric validation of EpCAM, Spp1 (mRNA detected by PrimeFlow technology) and CD63 expression (middle panels) and replicate MFI expression data of each of these markers by alveolar macrophages from adult unmanipulated Egr2 fl/fl and Lyz2 Cre/+. Egr2 fl/fl mice. Data are from one of two independent experiments with 5 (Cre -) and 4 (Cre + ) mice per group.
Bacterial levels (colony forming units, CFU) in the BAL fluid of Egr2 fl/fl or Lyz2 Cre/+. Egr2 fl/fl mice 14hrs after infection. Data are from 10 (Lyz2 Cre/+. Egr2 fl/fl ) or 12 (Egr2 fl/fl ) mice per group pooled from three independent experiments. *p<0. 05 (Mann Whitney test). I. Frequency (left) and absolute number (right) of CD11c hi CD11b lo alveolar macrophages and Ly6G + neutrophils in the BAL fluid of Egr2 fl/fl or Lyz2 Cre/+. Egr2 fl/fl mice 14hrs after infection. Data represent 10 (Lyz2 Cre/+. Egr2 fl/fl ) or 11 (Egr2 fl/fl ) mice per group pooled from three independent experiments. J. Expression of Marco, Wfdc10, Colec12 and C3 from the RNA-seq dataset (left panels). *p<0. 05, ***p<0. 001, ****p<0. 0001 (unpaired Student's t-test). Symbols represent individual mice in all graphs and error bars represent the standard deviation.
Acknowledgements. We are grateful to  Prof. Ping Wang  and  Dr. Su-Ling Li , QMUL, London for the kind gift of the Egr2-floxed strain and to  Prof. Rick Maziels ,  University of Glasgow  for the provision of Il4radeficient mice. Flow cytometry data were generated with support from the  QMRI Flow Cytometry and Cell Sorting Facility, University of Edinburgh. mRNA sequencing was performed by  Edinburgh Genomics ,  The University of Edinburgh. Edinburgh Genomics  is partly supported through core grants from  NERC  ( R8/H10/56 ),  MRC  ( MR/K001744/1 ) and  BBSRC  ( BB/J004243/1 ). Genewiz performed the bulk RNA-seq analysis. We would like to thank  Beth Henderson  for technical expertise in setting up 10X sequencing; Dr. Jordan Portman  for initial processing of the scRNAseq raw data; the  ShIELD (Sheffield, Edinburgh, Newcastle and Birmingham) consortium  for access to bacterial stocks; and  Dr.
Brian McHugh  and  Dr Manuel Sánchez García  for help and advice on bacterial studies. We would also like to thank the  Core Services and Advanced Technologies at the Cancer Research UK Beatson Institute , with particular thanks to the  Beatson Advanced Imaging Resource (BAIR). Finally, we would like to thank the  Bioresearch and Veterinary Services at the University of Edinburgh  for husbandry of our mice and other technical assistance. Servier Medical Art was used for the generation of some of the graphics. Funding. This research was funded by a  Sir Henry Dale  Fellowship jointly funded by the  Wellcome Trust  and the  Royal Society  [Grant number  206234/Z/17/Z  to C. C. B]. CGH is supported by a  University of Edinburgh Chancellor's Fellowship  and  CGH  and RC are supported by  Worldwide Cancer Research.
The Lyz2 Cre. Egr2 fl/fl line was originally generated with funding from the  Medical Research Council UK  ( MR/L008076/1  to S. J. J). GRJ is funded by a  Wellcome Trust   Clinical Career Development Fellowship  ( 220725/Z/20/Z ). SRW is funded by a  Wellcome Trust   Senior Clinical Fellowship  ( 209220 ). PTKS received funding from the  MRC  ( MR/N024524/1 ). FF and LMC are funded by  Cancer Research UK  core funding ( A23983 & A31287 ), and  Breast Cancer Now  (2019DecPR1424). This research was funded in whole or in part by The  Wellcome Trust  [Grant number  206234/Z/17/Z ], a cOAlition S organization. The author will make the Author Accepted Manuscript (AAM) version available under a CC BY public copyright license.
H. Bacterial levels (colony forming units, CFU) in the BAL fluid of Egr2 fl/fl or Lyz2 Cre/+. Egr2 fl/fl mice 14hrs after infection. Data are from 10 (Lyz2 Cre/+. Egr2 fl/fl ) or 12 (Egr2 fl/fl ) mice per group pooled from three independent experiments. *p<0. 05 (Mann Whitney test). I. Frequency (left) and absolute number (right) of CD11c hi CD11b lo alveolar macrophages and Ly6G + neutrophils in the BAL fluid of Egr2 fl/fl or Lyz2 Cre/+. Egr2 fl/fl mice 14hrs after infection. Data represent 10 (Lyz2 Cre/+. Egr2 fl/fl ) or 11 (Egr2 fl/fl ) mice per group pooled from three independent experiments. J. Expression of Marco, Wfdc10, Colec12 and C3 from the RNA-seq dataset (left panels). *p<0. 05, ***p<0. 001, ****p<0. 0001 (unpaired Student's t-test). Symbols represent individual mice in all graphs and error bars represent the standard deviation.
Spatial transcriptomics is now a mature technology, allowing to assay gene expression changes in the histological context of complex tissues. A canonical analysis workflow starts with the identification of tissue zones that share similar expression profiles, followed by the detection of highly variable or spatially variable genes. Rapid increases in the scale and complexity of spatial transcriptomic datasets demand that these analysis steps are conducted in a consistent and integrated manner, a requirement that is not met by current methods. To address this, we here present SpatialDE2, which unifies the mapping of tissue zones and spatial variable gene detection as integrated software framework, while at the same time advancing current algorithms for both of these steps. Formulated in a Bayesian framework, the model accounts for the Poisson count noise, while simultaneously offering superior computational speed compared to previous methods.
This step yields genes that are most relevant for downstream analyses, such as the definition of local niches in the tissue that support cellular differentiation and function, but knowledge about spatial variability in its own right can often provide biological insights, e. g. into cancer  (Thrane et al, 2018). However, existing methods for spatially variable gene detection typically consider the whole field of view within spatial transcriptomics dataset. As the field of view of spatial transcriptomics is growing and the methods are applied to increasingly complex tissues consisting of regions with different cell type compositions, naively applied spatially variable gene detection can no longer yield relevant insights, since the set of identified spatially variable genes primarily contains cell type markers that are not inherently spatially variable  (Cable et al, 2021).
Finally, we note that there is a lack of integrated software and workflows to be able to combine spatial variable gene selection and the identification of tissue regions. Here, we present SpatialDE2, a flexible framework for modeling spatial transcriptomics data. SpatialDE2 implements two major modules, which together provide for an end-to-end workflow for analyzing spatial transcriptomics data: a tissue region segmentation module and a module for detecting spatially variable genes. The tissue region segmentation is fast and provides improved usability compared to previous methods. In particular, the module is capable of automatically determining the number of tissue regions while employing an appropriate count-based likelihood. The module for the detection of spatially variable genes extends previous methods such as SpatialDE and SVCA by providing technical innovations and computational speedups.
Calculations of SpatialDE2 can be accelerated using GPUs, opening up the possibility to analyse larger datasets. We validate SpatialDE2's segmentation and spatial variable gene detection modules using simulated data and application to two real-world datasets, containing 10x Visium data of the mouse brain and human endometrium. In these applications, we demonstrate improved speed and robustness of both modules compared to previous approaches. SpatialDE2 provides an integrated solution for assessing spatial expression heterogeneity within tissue regions, thus providing a principled strategy for dealing with complex tissues and large samples.
Results SpatialDE2 implements an end-to-end workflow for the characterization of sub-regional spatial heterogeneity, by implementing two seamlessly integrated analysis modules: a tissue region segmentation module, and a module for spatially variable gene detection. (Fig. 1A ). Both modules directly model raw mRNA counts, as obtained from de-multiplexed spatial transcriptomics workflows, or imaging technologies as input. Optionally, SpatialDE2 can also operate on cell count estimates obtained from an additional deconvolution step  (Kleshchevnikov et al, 2020; Biancalani et al, 2020; Cable et al, 2021; Lopez et al, 2021; Andersson et al, 2020; Elosua-Bayes et al, 2021). Briefly, the spatial tissue region segmentation module is based on a Bayesian hidden markov random field, which segments tissues into distinct histological regions while explicitly accounting for spatial smoothness between neighbouring locations (Fig.
The spatially variable gene detection module models variance components of individual genes within identified regions using an appropriate count-based likelihood. While SpatialDE2 builds on Gaussian process regression, which is also used by methods such as SpatialDE  (Svensson et al, 2018) , SVCA  (Arnol et al, 2019), and SPARK (Sun et al, 2020) , the model generalizes these approaches by providing novel features, improved scalability, supporting multiple variance components and GPU computations (Methods, Table  S1 ). At the core, the model decomposes the expression variation into distinct structured components and a noise term that models random variability (Fig. 1C, Methods ). Depending on the design of one or multiple covariance matrices, different tests can be formalized that either quantify spatially variable genes, or identify genes that are regulated by cell-cell interactions.
Model validation using simulated data First, we used simulated data under the null hypothesis of no spatially variable gene expression (Methods) to confirm the statistical calibration of the spatially variable gene detection module (Supplementary Figure  S1A ). Briefly, similar to SpatialDE and SPARK, SpatialDE2 increases the sensitivity of its spatially variable gene detection by testing multiple kernel matrices for each gene. SpatialDE2 implements two strategies to estimate statistical significance: Each kernel matrix is tested separately and the p-values are combined using the Cauchy combination  (Liu & Xie, 2020) , or all kernel matrices are tested simultaneously using an omnibus test (Methods). The latter option is faster, since only a single test is conducted, however this strategy results in marginally reduced power (Supplementary Figure  S1B ).
We confirmed that both strategies yielded calibrated results (Supplementary Figure  S1A ). The omnibus test was used for all analyses in this paper. Next, we simulated true spatially variable genes with variable length scales, adapting empirical parameters from a 10x Visium mouse brain data set (Methods). We assessed the sensitivity (statistical power) of SpatialDE2, SpatialDE, and SPARK to detect true spatially variable genes. SpatialDE had the lowest sensitivity, whereas SpatialDE2 and SPARK yielded comparable results (Supplementary Figure  S1B ). By default, SPARK enforces positive definiteness of the kernel by performing an Eigen decomposition of the kernel matrix and setting negative eigenvalues to zero. Since its default periodic kernel is not positive definite, this results in poorly defined and uninterpretable kernels.
We therefore also included SPARK without eigenvalue clipping in our benchmark, observing consistent results. (D) Run time of SpatialDE2's tissue region segmentation module and a Leiden clustering workflow for semi-synthetic dataset of increasing size and when using alternative compute environments. Clustering/segmentation was based on 2,000 genes. Leiden denotes the scanpy workflow (Methods). Only SpatialDE2 supports GPU computations. (E) Run time of SpatialDE2's spatially variable gene selection module versus alternative methods. Considered is a dataset consisting of 200 genes for increasing numbers of locations and alternative computing environments. Only SpatialDE2 supports GPU computations.
2C ), and we assessed the sensitivity of both methods to variation in sequencing depth (Fig. 2D , Supplementary Figure  S2D, E ). Collectively, these experiments indicated that SpatialDE2 is at least as robust as the Leiden workflow. (B) Corresponding reference annotation of the mouse brain regions obtained from the Allen Brain Atlas  (Lein et al, 2007). (C) Assessment of the robustness of tissue region segmentations obtained using SpatialDE2 or Leiden clustering. Shown is the concordance across 100 bootstrap experiments, sampling sequencing reads with replacement from the full dataset (N=4. 5•10 7 reads total). Bar height denotes the mean split/join distance compared to the results obtained on the full dataset; error bars denote plus or minus one standard deviation. (D) Assessment of the robustness of the SpatialDE2 tissue region segmentation to downsampling of sequencing reads (out of N=45x10^6 reads total).
The total number of sequencing reads in the mouse brain dataset was downsampled to the indicated amount. Shown are the segmentation results. (E) Results from the identification of spatially variable genes within the tissue regions identified from the segmentation step as in (A). Bar heights denote the number of spatially variable genes within each region (FDR<0. 1%; out of 12,682 genes assessed). (F) Selected examples of spatially variable genes identified in the thalamus. Shown is normalized relative expression of the indicated genes.
3E ). Among these genes was the progesterone receptor PGRMC1, in agreement with the fact that the functional layer of the endometrium differentiates (decidualizes) in response to hormones. SpatialDE2 by design takes spatial omics profiles as input, thus rendering the model applicable to a range of different technologies that yield expression estimates that can be explained by a count-based likelihood. This includes both sequencing-based assays but also imaging technologies (see discussion in SpatialDE1  (Svensson et al, 2018) ). However, in particular for Visium or other technologies that do not achieve true single-cell resolution, the relatively coarse segmentation may result in limitations to assigning specific tissues to clusters, as seen in the endometrium data set with the 'glands (border)' cluster.
Motivated by this limitation, we set out to explore how SpatialDE2 can be combined with cell type count estimates obtained from computational deconvolution workflows that leverage reference scRNA-seq datasets to estimate cell type abundance. Specifically, we applied SpatialDE2 to the cell type abundance estimates obtained by cell2location on the endometrium dataset  (Garcia-Alonso et al, 2021). This resulted in a more fine-grained segmentation compared to applying SpatialDE2 directly to gene-counts (Fig. 4A ). For example, SpatialDE2 now split the basal layer into three distinct regions (numbered 1,5, and 8 in the figure). Spots belonging to these regions were also characterized by distinct clusters in the gene expression space (Fig. 4B ), indicating that there exist relevant phenotypic differences between these regions.
Discussion Here, we present SpatialDE2, a versatile toolbox for analyzing spatial transcriptomics data. At the core of the method are two analysis modules -region segmentation and spatially variable gene identification. Both modules offer important advantages compared to existing methodology. Moreover, the modules are compatible and can be combined efficiently within one coherent software workflow. The region segmentation module uniquely allows to account for the spatial structure of tissues, directly operates on RNA count data, and it can be adjusted by a single intuitive parameter. At the same time the module is as efficient as conventional Leiden clustering, which ignores the spatial coordinates. Additionally, SpatialDE2 offers the option of GPU-accelerated computations. Similarly, the spatially variable gene direction module offers orders of magnitude faster computation than previous methods like SpatialDE  (Svensson et al, 2018)  or SPARK  (Sun et al, 2020) , while offering comparable or better statistical power.
SpatialDE2 also retains the distinguishing features of these kernel-based methods, such as the ability to test for specific types of expression pattern by designing corresponding kernels. The two modules work hand in hand, which allows efficient implementation of a workflow consisting of first segmenting the tissue slice followed by detection of spatially variable genes within each region. We illustrated this workflow on the mouse brain, a well-characterized tissue suitable for assessment of performance. On this data set, tissue region segmentation performed comparably to a Leiden clustering workflow and we were able to recover known biology, such as the major brain regions. We also applied the SpatialDE2 workflow to a human endometrium data set, a much less well-characterized tissue. Here, our tissue region segmentation proved superior to Leiden clustering.
Acknowledgements We thank  Anna Arutyunyan  for kindly sharing the endometrium Visium data and cell2location results and for critically reading the manuscript. We also thank members of the  Stegle lab  for helpful comments and discussions. The Stegle research group was further supported by core funding from  EMBL , the  German Cancer Research Center , and the  European Commission  ( ERC  project  DECODE ,  810296 ).