dataesr/scientific-paragraphs-categorization huggingface dataset:
Since the aim was to test the enrichment of these murine fingerprints on human expression data, the murine fingerprint gene symbols were converted into those of their human orthologs, using the BioMart tool from ENSembl  [42]. In order to test the statistical significance of the enrichments obtained with BubbleMap, the GSEA algorithm performs permutations either of the samples or of the gene sets. The human expression dataset is composed of cell types profiled in triplicates, which is not sufficient to perform a sample permutation test as it requires at the very least 5 replicates. Hence, we performed the analysis using the gene set permutation option. The p-values of each enrichment were corrected for multiple testing, to generate FDR values and thus allow for the first time rigorously comparing all the results with each other for many GSEA across multiple pairs of conditions, and hence to allow globally interpreting the BubbleMap (See Materials and Methods).
Good sources of additional gene sets can be curated public databases such as MSigDB or SMD  [3] [4] [5]. In this case study, we added twice the number of the starting gene sets by randomly picking gene sets from collection c3. all. v4. 0 from MSigDB. To select all possibly interesting enrichments as a discovery strategy to increase likelihood of discovering novel information, GSEA developers recommend to set the FDR evaluating the statistical significance of the enrichments to a maximal threshold of 0. 25  [1]. In our specific study case which is distinct from a discovery strategy but rather a confirmation study where the aim is to identify cell type equivalence relationships that have already been demonstrated in order to validate the relevance of our method, we chose a more stringent 0. 10 as the threshold for the FDR.
Hence, enrichments with a FDR > 0. 10 were considered non-significant and displayed as empty circles. It took about 1 h and 15 min to run the BubbleMap analysis (5000 gene set based permutations) using the murine transcriptomic fingerprints obtained from GeneSign merged together with the randomly picked gene sets (68 gene sets in total), applied to the expression dataset of human immune cell types composed of 21 arrays representing 7 phenotypes (42 pairwise comparisons), on a personal computer with a 2. 4 GHz i7 processor and 8 GB of RAM memory allocated (see Additional file 3: Table  S1  for an estimation of the computational costs of Bubble-Map analyses). Once the analysis has been performed and the results displayed as a BubbleMap, we used the filtering tools for selecting the gene sets and pairwise comparisons of interest.
4 ). Thus, human neutrophils were not found to be homologous to any of the mouse immune cell types used in our analyses, in consistency with the lack of neutrophils in the mouse dataset used. However, the murine CD11b + cDC fingerprint was found significantly enriched in human neutrophils as compared to all other human cell populations examined except CD1c + cDC. This enrichment pattern may reflect the strong myeloid signature of mouse CD11b + cDC and human CD1c + cDC, which is well documented and can make difficult the discrimination between these cells and monocyte-derived DC. Altogether, these results confirm in the case of immune cells the existence of evolutionary conserved molecular signatures specific to cell types  [40] , and demonstrate how our analysis workflow using BubbleGUM powerfully enables researchers to exploit these signatures for identifying homologous cell types in different species.
Conclusion The combined use of GeneSign and BubbleMap allowed us in a very simple way to generate transcriptomic fingerprints for murine steady state splenic immune cell types and to assess their enrichments in human blood immune cell types. This analysis showed that the transcriptomic fingerprints for a specific murine immune cell type are systematically enriched in the human immune cell type previously shown/proposed to be its homolog whatever other immune cell type this population was compared to. Hence, we developed a dedicated BubbleGUM bioinformatics tool in order to implement, in a processive, rigorous and easy to interpret way, a strategy for in-depth Omics data mining based on i) transcriptomic signature generation and ii) integration and visual comparison of multiple GSEA. We illustrated the functioning and utility of this strategy and software through their use to confirm homologies between mouse and human immune cell types.
In addition, this type of strategy has also been used to identify among several experimental animal models available those that best mimic human pathologies  [16] [17] [18] [19] 21]. The spectrum of questions to which BubbleGUM can contribute to answer is even considerably larger, since its use can be extended to comparing different types of Omics data, for example to compare mRNA and protein expression in a simple but informative manner, or to examine correlations between mRNA expression and epigenetic modifications at a global scale (unpublished data). Hence, our BubbleGUM software should considerably facilitate integrative analysis of Omics data in many research areas.
Availability and requirements Project Name: BubbleGUM Project home page: http://www. ciml. univ-mrs. fr/appli cations/BubbleGUM/index. html Fig. 4  No correlation between the murine cell-specific signatures and the transcriptome of human neutrophils. The BubbleMap of human neutrophils compared to other human immune cell types, using the murine cell-specific signatures, does not reveal any consistent enrichment pattern that is systematically enriched in the neutrophils Operating system(s): Linux, Mac, Windows Programming language: Java Other requirements: Java 7. x (or Java 1. 7. x for Linux) or higher (64-bit); a minimum of 4GB of dedicated RAM memory. License: European Union Public Licence (1. 1) Any restrictions to use by non-academics: written permission from the authors needed.
Acknowledgements We thank the  GSEA team  for allowing us to use their code, as well as  A. Baudot ,  E. Pollet ,  R. Chelbi ,  M. Masse  and  A. Fries  for fruitful discussions and/or intensive tests or feedbacks. This work was supported by the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013 Grant Agreement number  281225  to MD for the  SystemsDendritic  project). SC is partly funded by  ANR  grant  n°ANR-11-DPBS-0002.
Lipidic cubic phases (LCPs) have emerged as successful matrixes for the crystallization of membrane proteins. Moreover, the viscous LCP also provides a highly effective delivery medium for serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs). Here, the adaptation of this technology to perform serial millisecond crystallography (SMX) at more widely available synchrotron microfocus beamlines is described. Compared with conventional microcrystallography, LCP-SMX eliminates the need for difficult handling of individual crystals and allows for data collection at room temperature. The technology is demonstrated by solving a structure of the light-driven protonpump bacteriorhodopsin (bR) at a resolution of 2. 4 A ˚. The room-temperature structure of bR is very similar to previous cryogenic structures but shows small yet distinct differences in the retinal ligand and proton-transfer pathway.
The first serial crystallography experiment at a synchrotron yielded a 2. 1 A lysozyme structure by merging single frames from microcrystals injected randomly into the X-ray beam in a glass capillary  (Stellato et al. , 2014). Our LCP windowless injector allows the stream velocity to be slowed down to a rate of 0. 05-0. 15 mm ms À1 , which allows 10-100 ms exposure times and efficient use of the sample. Here, we demonstrate that this makes it possible to perform LCP microjet-based serial millisecond crystallography (SMX) using synchrotron radiation, similar to SFX at an XFEL. The key advantages of this method are: (i) crystal injection using the LCP combined with a microfocus beamline allows diffraction data to be collected at room temperature, and hence crystal freezing and difficult crystal handling steps such as mounting crystals in a loop are not necessary; (ii) thousands of crystals can be screened in a short time and with less than a milligram of protein; (iii) microfocus beams at storage-ring sources are widely available and hence beam access is unlikely to limit SMX; and (iv) the method is well suited for time-resolved diffraction studies on the microsecond to millisecond timescale.
Data collection and processing Serial crystallographic data were collected at the ESRF Microfocus Beamline (ID13; Grenoble, France) in the setup described in the Results section. Alignment of the X-ray beam onto the LCP stream was facilitated with a grid scan of the area around the tip of the injector. Diffraction patterns were collected from randomly oriented crystals with 10-50 ms exposure times at a rate of 10-17 Hz. Due to random failures in the Rayonix MX-170 CCD detector, the upper left quadrant was excluded during data analysis. The detector was set to 4 Â 4 binning mode with a pixel size of 177 mm and a frame size of 960 Â 960 pixels. Because of overheads due to saving data on the ESRF data server, the frame rate was limited to 17 frames s À1. Collected images (details in Fig. 1 ) were preprocessed using Cheetah  (Barty et al.
, 2014)  to exclude images without diffraction patterns. Higher hit rates and resolution were observed for crystals sized 20-40 mm. The CrystFEL program suite  (White et al. , 2012)  was used for data processing. The conventional Cryo data set was collected at the PXI beamline of the Swiss Light Source (Paul Scherrer Institute, Villigen, Switzerland) in a cryostream at 100 K (details in Table. 1). The diffraction images were processed and scaled using XDS  (Kabsch, 2010) , followed by merging using Aimless  (Evans & Murshudov, 2013).
Model building and refinement A single molecular replacement solution was found using the SMX data set and the structure of sensory rhodopsin II [PDB code 1h68  (Royant et al. , 2001) , without ligands] as a search model in Phaser  (McCoy et al. , 2007). Initial phases were used to rebuild the bacteriorhodopsin model automatically with Phenix. autobuild (Adams et al. , 2002) , using simulated annealing refinement between iterative building steps. Manual building in Coot  (Emsley & Cowtan, 2004 ) was used to complete the autobuild model, except for residues 1-4 and 234-249, and four side-chains for residues Q75, S158, K172 and R227. Further refinement was carried out using PDBREDO  (Joosten et al. , 2012) , which suggested one TLS group for the whole protein chain. In a final round of model building and refinement in Refmac5  (Murshudov et al.
, 2011) , the model was completed with ten water molecules, five lipid fragments and all-trans retinal. The retinal was refined with restrained geometry of the Schiff base at the covalent link to Lys216. The conventional Cryo data set was phased with Phaser, using bacteriorhodpsin [without ligands, PDB code 2ntu  (Lanyi & Schobert, 2007) ] as a search model. A single solution was found and the model was completed and refined using Refmac5. Retinal, 30 water molecules and eight lipid fragments were included in the last rounds of refinement. The unresolved region included the first four and last 17 residues, similar to the SMX structure. Moreover, a loop consisting of residues 157-163 was not included in the model obtained with the Cryo data, while for the SMX data it could be modelled into a weak electron density.
Poorly resolved side-chains of K30, R164 and K172 were also not included in the model. The statistics are listed in Table. 1. The protein structures determined using the SMX and Cryo data sets have been deposited in the PDB with codes 4x31 and 4x32, respectively. Data collection and refinement statistics for the SMX and CRYO bR structures. The upper inset shows the development of the correlation between the two indexing possibilities over the number of crystals used to resolve the indexing ambiguity. The middle inset shows zone-axis plots of the data before and after solving indexing ambiguity. Colours are proportional to the square root of the intensity (i. e. I 1/2 ). The lower inset plots the signal-to-noise ratio, expressed as I/(I), against the resolution of the SMX data.
Sample preparation and LCP injection Bacteriorhodopsin (bR) was the first protein for which the structure was solved (Pebay-Peyroula et al. , 1997) from crystals grown in the LCP  (Landau & Rosenbusch, 1996) , using data collected at the ID13 microfocus beamline. Since LCP-grown bR crystals diffract to high resolution and can be easily visualized due to their purple colour, bR is an ideal protein to demonstrate LCP-SMX at synchrotron sources. To produce a sufficient number of bR crystals for our experiment, we adapted previously published crystallization conditions  (Nollert, 2004 ) to a setup using 100 ml gas-tight syringes (Supplementary Fig. S1 ), similar to that described elsewhere  (Liu, Ishchenko & Cherezov, 2014; Liu, Wacker et al. , 2014). Once crystals had formed, excess crystallization buffer was removed and the residual buffer was absorbed by adding further monoolein.
with >10 Bragg peaks/total number of images) of 0. 5-2%, which is somewhat lower than the hit rates of 3-8% reported for similar experiments with different samples at the LCLS  (Liu et al. , 2013; Weierstall et al. , 2014). This is probably due to the lower crystal density in our setup, as crystal size and crystal density were negatively correlated in our crystallization screening and we achieved the best diffraction with crystals of 20-40 mm, much larger than what would be ideal for data collection at an XFEL. Together with the lower data acquisition rate of 10-17 Hz at the ESRF compared with 120 Hz at the LCLS, the lower hit rate meant that the collection of this data set took 3 d. Nevertheless, only 0. 8 mg of protein in 200 ml of LCP was needed. Occasionally, two or three consecutive hits were recorded on some of the larger (40-50 mm) crystals.
Bragg spots appeared and disappeared within this sequence of consecutive images, indicating that the rotational diffusion of crystals in the LCP within the 80 ms between two exposures is larger than their mosaic spread. To investigate this further, a computer program was written to compare the orientations of crystals in adjacent frames using the data stream output from CrystFEL. For the fraction of data acquired with a 25 ms exposure time (81% of the total frames), 1088 frames (26% of the indexed patterns) were found to be part of a rotation series. The mean series length was 2. 2 frames and the maximum series length was 4 frames. Such a series of consecutive diffraction patterns might be useful for indexing and integration, as it resembles a small wedge of rotation data similar to those typically collected in conventional crystallography.
Data processing and map calculation We collected 1 343 092 images, of which 12 982 were classified as hits using the Cheetah program  (Barty et al. , 2014) , giving an average hit rate of $1%. A large fraction of the frames were found to exhibit artifacts in one quadrant of the detector, and this quadrant was therefore ignored for all stages of analysis. The unit-cell parameters were determined to be a = b = 62. 79 A ˚and c = 109. 67 A ˚in space group P6 3 during initial indexing of a subset of the data, consistent with the known lattice of bR crystallized in LCP. Of the initial hits, 5691 images were successfully indexed and integrated by CrystFEL (Version 0. 5. 3a+e2c7dbd5) without difficulty. However, the space group of bR crystals is subject to an indexing ambiguity [see  White et al. (2013)  for an extensive discussion], which was resolved by CrystFEL using an algorithm related to one recently developed for this purpose  [Brehm & Diederichs (2014) ; see  Liu & Spence (2014)  for a solution based on an expectation maximization algorithm], prior to merging the individual intensity measurements for each symmetrically unique reflection according to point group 6/m (i.
It is possible that this improvement in crystal quality is due to a change in crystallization conditions, since we used polyethylene glycol as precipitant to avoid high concentrations of salts for future XFEL experiments. A comparison of the two data sets is shown in Fig. 1. An important consideration for every structure determined by molecular replacement is the potential impact of model bias, which may hide differences between the model used for structure determination and the true structure. To limit the impact of model bias, we used the related sensory protein rhodopsin II [PDB code 1h6b  (Royant et al. , 2001) , $30% sequence identity with bR] for molecular replacement with the SMX data (supplementary Fig. S3 ) and subjected the solution to automatic model building using simulated annealing refinement in Phenix  (Adams et al.
Comparison of Cryo and SMX structures The synchrotron cryocooled bR structure was solved using molecular replacement with ground-state bR (PDB code 2ntu;  Lanyi & Schobert, 2007). Overall, structure and the bR structure collected by SMX at room temperature are very similar (Fig. 3 ), with a C root mean-square deviation of 0. 54 A ˚. The distribution of crystallographic B factors is comparable between the two structures, but the B factors are slightly higher in the SMX bR structure, probably because of the lower resolution or increased thermal motion of the protein at room temperature. We observed weak electron density for the loop between helix E and helix F, and were thus able to include this loop in the SMX bR structure despite the lower resolution. Hierarchical cluster analysis  (Wickstrand et al.
, 2014)  reveals average differences in the internal distance matrix (S ij ) of the order of 0. 2 A ˚relative to most published structures of the bR ground state (Fig. 4 ). This difference is primarily due to a small perturbation of helices D, E and F away from helices A and B relative to the structure solved with the Cryo data. This effect potentially reflects an impact of cryocooling, compressing the bR structure slightly, rather than differences in data collection, since it is well known that cryocooling can introduce small structural perturbations. Analysis of data from 30 proteins  (Fraser et al. , 2011) , for example, indicated that cooling changes the side-chain conformations for 35% of surface-exposed residues. Comparison of serotonin receptor 2B (5-HT2B) crystal structures collected using microcrystallography under cryoconditions  (Wacker et al.
Today, around 95% of protein structures are determined from cryocooled crystals. While in our case the structural differences between room-temperature and cryogenic data collection were small, in some cases cryogenic cooling can change the dynamic behaviour of proteins and may lead to structural artifacts  (Fraser et al. , 2011; Keedy et al. , 2014). The reason why cryogenic data collection is still so dominant is that cryocooling reduces radiation damage, which is the major factor limiting the amount and quality of structural information that can be obtained from a protein crystal  (Garman, 2010b). Primary radiation damage is a result of the X-ray photoionization of atoms in the crystal or surrounding liquor, and the subsequent rapid cascade of electron collisional ionization that takes place in several hundred femtoseconds, resulting in lowenergy solvated electrons and hydroxyl radicals.
This analysis sorts according to the average of the absolute value of the difference between two internal distance matrices [S jj (A ˚)] calculated on C atoms  (Wickstrand et al. , 2014). PDB codes are given for all deposited wild-type structures of bR in its resting state. LCP bR structures are marked in purple. The room-temperature SMX structure (bRSMX) and the structure of bR solved here using conventional data collection and cryocooling (bRcryo) are marked in red. Inset: The internal distance matrix for bRSMX-bRcryo shows that cryocooling compresses helices A and B slightly towards helices D, E and F. method for data collection, mainly due to the modifications needed at synchrotron beamlines, rotational limitations, background from conventional crystallization plates and radiation damage.
The LCP injector used in our SMX experiment provides a constant stream of fresh crystals, so that each of the several thousand crystals contributing to the final data set is exposed for only 10-50 ms. The total radiation dose to the exposed region of a single crystal is thus only about 0. 7 MGy, compared with the 28 MGy deposited during collection of the conventional data set (calculated using RADDOSE-3D;  Zeldin et al. , 2013). Per crystal, the dose thus remains below the Henderson-Garman safe dose limit of 1 MGy at room temperature and 30 MGy for frozen samples  (Garman & McSweeney, 2007). Indeed, we could not detect radiation damage by investigation of radiation-sensitive residues in the SMX structure, although it has been argued that very subtle shifts near the retinal Schiff base can be observed in high-resolution cryo structures of bR at a dose as low as 0.
06 MGy  (Borshchevskiy et al. , 2014). By contrast, the structure obtained from cryocooled bR crystals (28 MGy dose) showed weak density for several aspartate residues (Fig. 3 , upper right inset), an indication of mild radiation damage that could possibly be avoided by further truncation of the data. Nevertheless, this comparison shows that serial injection of crystals at a synchrotron using an LCP injector allows collection of data at room temperature with minimal radiation damage. A further potential application for LCP-SMX at room temperature is time-resolved crystallography. Cryocooled crystals have been used extensively for low-temperature trapping of bR intermediates [reviewed by  Wickstrand et al. (2014) ] but cannot be used for genuinely time-resolved pumpprobe experiments. Room-temperature LCP-SMX can be adapted for time-resolved diffraction studies by using laser pulses to photoactivate the microcrystals and varying the time of arrival of the photoactivating laser pulse relative to when the X-ray image is recorded.
, 2009)  or X-ray imaging  (Warren et al. , 2013)  are required. Serial injection of microcrystals by LCP-SMX, as demonstrated here, has the potential to simplify this process by eliminating pre-exposure, handling and centring of crystals. Improvements in sample preparation, as well as the use of next-generation single-photon counting X-ray pixel detectors with a higher frame rate beyond 1 kHz frequency and a shorter readout dead time in the microsecond range, will allow matching of the rate at which crystals traverse the beam with the desired exposure time, resulting in more efficient data collection. The practically zero readout noise of modern detectors compared with the CCD detector we were limited to in our experiment will further increase the achievable resolution. Another important factor is background scattering from the stream of LCP.
Acknowledgements The authors thank  Oleksandr Yefanov  and  Anton Barty  for discussions on the processing of serial data, and  Guido Capitani  and  Wolfgang Brehm  for remarks on how to handle the indexing ambiguity. For the implementation of auxiliary infrastructure at the ID13 beamline, we thank  Lionel Lardie  `re,  Michael Sztucki ,  Britta Weinhausen  and  Thomas Dane. The Rayonix MX-170 CCD area-detector was on loan from  Theyencheri Naryanan  at the  ESRF ID02 beamline. We thank  Anuschka Paulhun  and  Vincent Olieric  from the  Macromolecular Crystallography group at the Swiss Light Source  for support during collection of the conventional cryogenic data set and in situ testing of crystals. This work was financially supported by the  Swedish Research Council (PBa) , the  Knut and Alice Wallenberg Foundation (PBe) ,  NIH  grant  R01GM108635  (VC), the  Ro ¨ntgen-A ˚ngstro ¨m-Cluster  project ( 05K2012 ) of the  German Bundesministerium fu ¨r Bildung und Forschung (BMBF)  (DO and HC) and  NSF   STC award   1231306  (DJ, DW, NZ, GN, HL and UW).