dataesr/scientific-paragraphs-categorization huggingface dataset:
The sectioning direction is indicated with an arrow. As soon as the first NIRB marks were spotted in the thick sections, we proceeded with obtaining thin 240 nm sections for electron tomography ('Correlation+EM imaging ROI'). These sections were not only employed for EM imaging of the ROI, but also provided the structural features that were used for correlation between 2PEM and EM. B. Graphic representation of the landmark-based correlation procedure. The prisms with the blue and green ribs represent similar 3D volumes that differ in size, shape and orientation, representing the samples pre-and post-EM-processing. In order to predict the position of the ROI (ball, visible in volume 1) in volume 2, similar points (landmarks) in both datasets are identified ('Correlation'). These landmarks are overlaid in 3D ('Registration') enabling warping of volume 1 into volume 2 and thus also projecting the position of the ROI in volume 2 ('Docking').
Electron Microscopy Imaging The thin sections were imaged with a CM120 transmission electron microscope (TEM, FEI Company) operating at 120 kV. Images were obtained with a Keen View CCD camera (Soft Imaging Solutions, Olympus), running under iTEM (Olympus) software. Electron tomography was performed with a Tecnai F30 Field Emission Gun TEM (FEI Company) operating at 300 kV and equipped with an Eagle 4K camera (FEI Company). The F30 was controlled by Tecnai User Interface (PEOUI) and Tecnai Acquisition (TIA) software. Single-axis tomograms were obtained using SerialEM  [42] , and reconstructed in eTomo, part of the IMOD software package  [43]  (Boulder Laboratory, University of Colorado).
Image Processing Fluorescence images were processed in Fiji, part of ImageJ  [44] , IMARIS software (Bitplane), and in Adobe Photoshop CS6. Photoshop CS6 was also used to process the EM images. 3D registration of the serial sections and of the tomograms was performed in 3dmod, part of the IMOD software package  [43]  (Boulder Laboratory, University of Colorado), and the movies were produced in 3dmod, Fiji and Amira (FEI Company). Alignment, transformation and registration of the 3D models of the 2PEM and EM datasets were performed in Amira.
Sequential LM/EM to Retrieve the Cell of Interest Although the NIRB markings outline the area of the sample that contains the ROI, they do not reveal how deep within the tissue the cells of interest can be found. To retrieve the tumor cells, it is therefore necessary to carefully monitor the progression to the ROI while serial sectioning through the sample. As an initial method to correlate between 2PEM and EM, we chose to use a classic approach for the correlation  [47] , consisting in alternating between semi-thin (500 nm) and ultra-thin (60 nm) sections of the area of interest. LM images of the semi-thin sections are used to visualize the global anatomy of the tissue, which can then be compared to the 2PEM dataset. When the cells of interest are localized on the semi-thin sections, they can be easily retrieved in the consecutive thin sections and imaged by EM.
These features were also notable in the 2PEM dataset and could thus be used as reference points for correlating the volume acquired via 2PEM with the one from the resin embedded sample. While closely monitoring the progression through the sample, the vessels visible in the 2PEM stack ( Figure  2A ) were identified in the thick sections. From the 2PEM z-stack we could predict that our cell of interest should appear approximately 3. 5 mm below this first set of serial sections. We then collected a sequence of thick and thin sections of the area of interest (Figure  2C ). Overview images of the LM sections were correlated to the 2PEM z-stack (Figure  2A , Figure  2C, 'Correlation ROI',  and D ). Finally, the cell of interest was identified based on its shape and its position relative to one of the vessels (Figure  2A  and 2D, arrowhead).
Serial EM of Invading Tumor Cells The procedure described above (Figure  2C ) is an efficient way to correlate FM and EM. However, it does not allow full three-dimensional reconstruction of the tumor cells with EM since it dedicates part of the volume of interest to sections for LM. In order to enable EM imaging of the complete ROI, it is necessary to obtain serial EM sections of the full volume. In this second approach we focused on tumor cells that were in close proximity to a large blood vessel (Figure  3A  and Movie S3). For a better understanding of their distribution within the selected volume, the 2PEM dataset was segmented and the cells of interest rendered in green and numbered (Figure  3B ). Orthogonal views (XZ) of the 2PEM dataset ( Figure  3C ) reveal the position of the cells of interest (circled) relative to the vessels (red, Evans Blue) and to the collagen-rich skin surface (blue, SHG).
To approach the ROI (Figure  3D , 'Approach + Correlation'), serial thick sections were imaged by LM and aligned. The image stack was then segmented (Figure  3E  and F, Movie S3) to highlight the NIRB marks (purple), hair follicles (orange) and vessels (red) using 3dmod  [43]. The resulting 3D model of the sample features (Figure  3F ) was then correlated to the model from the 2PEM dataset. We could monitor the block-face position within the sample by overlaying the two 3D models ( Figure  3G  and Movie S3). The three vessel bifurcations that could also be recognized in the 2PEM dataset (Figure  3G , asterisks, and Figure  S1A , V1-3) were key features to try to predict the position of the cells of interest. Three of the cells  (3, 5 and 6)  are in close proximity to these vessels, but slightly deeper into the tissue (about 1 to 2 mm as estimated from the measurements done on the 2PEM data-set).
Having approached the cells of interest, we switched to producing serial EM sections (Figure  3D , 'EM imaging ROI'). Based on the 2PEM dataset, we could anticipate the cells of interest being within 50 mm in depth below the mentioned vessel bifurcations (in the z-range 73-123 mm of the imaged volume, Movie S3). To ensure that we could image all selected cells with EM, we obtained. 170 serial sections, approximately 300 nm thick that were suitable for EM tomography. This 'library' of sections represented the complete volume in which the cells of interest could be expected. By aligning low magnification TEM images of these sections to the 2PEM z-stack using Photoshop, the xy positions of the cells could be determined. Using these coordinates as a guide, we could retrieve the selected tumor cells (Figure  3H, 1 2 3 4 5 6 ).
The correlation was further confirmed by the shape similarity of the cells and their position relative to the vessel. Serial sectioning of the sample enabled us to reconstruct 3D EM models of the cells of interest through segmentation of the cells' plasma membrane (Figure  3I-K ). This provided ultrastructural information of the tumor cells, and further confirmed the correlation of the two datasets (compare Figure  3I  and K ). Importantly, the gain of resolution between 2PEM and EM can be appreciated even though EM is performed here at low magnification (Figure  3I-K ). For example, while the cell appears to be very close to the blood vessel in the 2PEM dataset (Figure  3I ), serial EM reveals that it is actually still physically separated at least 5. 5 mm, from the lumen of the vessel by both cellular and a-cellular material (Figure  3J  and K ).
Identification of corresponding cells and other structural features (vessel branches) in both datasets allowed producing simplified 3D maps of the 2PEM zstack and the serial section dataset. For both datasets, the relative positions of structural features, i. e. cells and vessel branches (Figure  S1A ), were plotted within a 3D coordinate system. In the 3D Analysis Software Amira, it was possible to simultaneously visualize both maps in 3D and to select common features, 'landmarks', in both datasets (Figure  S1B ). Together, the coordinates of the selected points form two paired 'landmark-sets', which are the basis of the correlation between the two datasets. Using Amira's LandmarkWarp module, we performed a transformation of one map to fit into the other by attempting to overlay the paired landmarks in 3D.
Applying a 'rigid' transformation method will optimize the warp without any deformations, but solely by translation and rotation of the dataset. The 'Bookstein' transformation method does allow additional non-linear transformations of the model, including stretching and scaling, in order to make both landmarks-sets overlap. Comparing the two different transformation approaches to fit the 2PEM in the serial sections model reveals the differences between both datasets. Using rigid transformations, it was not possible to overlay the landmarks in 3D (Figure  S1C ); this could only be achieved by the non-linear transformation enabled by the Bookstein method (Figure  S1D ). This finding confirms that, as expected, non-uniform changes are introduced while processing living tissues for EM. Therefore, the position of the cells of interest in the 2PEM dataset cannot be directly translated to their location into the resin block and therefore in the stack of serial sections.
As a consequence, the strategy for targeting cannot rely on a 3D mapping by LM from semi-thin sections as shown previously. In the situation illustrated in Figures  4  and 5 , the best strategy is therefore to perform a minimal approach of the region of interest by semi-thin sections (500 nm), that would expose the NIRB markings only, and then to collect the rest of the tissue by serial sections for TEM (Figure  5A ). Using this approach we obtained a complete 3D TEM dataset of the ROI and its surroundings, which enabled full volume correlation between 2PEM and TEM. From both the 2PEM and TEM dataset we could build precise 3D maps of the tissue, and the landmarks therein. Having identified corresponding landmarks, we could overlay the two datasets through 3D registration. A simplified cartoon of the steps involved in this approach is shown in Figure  5B.
As revealed by nonlinear 2PEM (SHG) and TEM, the collagen fibers form dense within the tissue (Figure  5C ). They are distributed as an organized meshwork that can be used as landmarks in both modalities. As the 2PEM dataset also provides the position of the cell of interest relative to the collagen fibers (Figure  5B , dashed lines), the registration transfers the predicted position of the cancer cell within the EM dataset. Using Amira, the collagen fibers were modeled from the 2PEM dataset and from the EM z-stacks (Figure  5D  and E ). Additionally, we also segmented the cell of interest in the 2PEM dataset (Figure  5D ). Visualizing both 3D models in a shared 3D space in Amira enabled to identify corresponding points in both datasets, e. g. cross-points between collagen fibers. These similarities in both datasets could be selected with 'Landmark (2 sets)'.
Using Amira's Landmarkwarp module, the 2PEM collagen model with the ROI was then warped into the EM collagen model. This required non-linear transformations of the 2PEM model (Figure  5F ). This finding is in agreement with our efforts to align a 2PEM and serial section 3D maps of structural reference points (Figure  S1 ). Here, it was not possible to overlay the corresponding points from the pre-and post-EM-processing maps without performing non-linear transformations. In Amira's virtual 3D space, the collagen models were linked to the image zstacks from which they are segmented. Warping the 2PEM model to fit within the TEM model therefore also results in the projection of the ROI on the series of TEM images from the serial sections (Figure  5G ). Having established the approximate position of the cell within the series of serial sections, highresolution images could be made in this area (Figure  5H ).
In this paper, we combine and synergize previously published strategies in order, for the first time, to perform accurate and reliable correlative light and electron microscopy of tumor cells in vivo upon non-invasive 2PEM imaging. One consists in recording the volume of the living sample to build a three-dimensional map of the region of interest  [15, 52] , which is used to predict its position in the resin block. Another relies on artificial landmarks produced with a near-infrared laser to highlight the region of interest directly on the hydrated sample  [12] [13] [14]. Finally, we improved a strategy that we used before  [15, 52]  consisting of recapitulating the local anatomy of the tissue with serial sections in order to retrace the position of individual objects within the EM dataset  [16].
Our method of correlation has three variations. With the first, sequences of thick and thin sections enable quick screening of the sample for the ROI, by comparing the 2PEM model to the 3D reconstruction built from the LM acquisitions of serial thick sections. Upon identification of the tumor cell in these sections, its ultrastructure is recorded by TEM from the adjacent thin section ( Figure  2 ). A disadvantage of this approach is that it allocates a significant fraction of the ROI to the model reconstruction by serial LM imaging. Although these thick sections could in principle be re-embedded and re-sectioned for EM, fullcell reconstruction will not be possible and crucial structures may be lost. With the second strategy, a fraction of the embedded tissue is used to create a partial 3D model, which is correlated to the 2PEM dataset of the same area.
The major advantage is the preservation of the cells of interest for further TEM imaging. Moreover, it facilitates an accurate estimation of the depth at which cells should be expected within the resin block. Full volume correlation allows determining the position of the ROI (Figure  5B ). Using the 3D imaging software Amira  [53] , the registration of the two datasets required non-linear transformations of the 2PEM model to fit into the model obtained from the serial TEM images (Figure  S1 ) reflecting the sample distortions caused by the preparation methods  [54]. Chemical fixation, dehydration and resin embedding each influence the volume orientation of the sample. During chemical fixation, GA will cause around 5% shrinkage of the sample  [55] , whereas OsO 4 fixation will result in swelling  [54, 56, 57].
Subsequent dehydration and resin embedding will cause further shrinkage to the specimen, which can be minimized by exposing the sample as briefly as possible to the solvents  [55] , e. g. by microwave assisted embedding. Importantly, the sample distortions are never homogenous throughout the tissue due to differences in biological material and infiltration efficiency of the chemicals. This explains why the 2PEM dataset does not linearly corresponds to the TEM dataset. As a consequence, the prediction of the position of one particular structure inside the resin-embedded sample has a limited accuracy (in the order of 20 mm). Taking this into consideration, the progression through the sample has to be performed with great care and, upon approaching the ROI, thin sections for EM should be collected systematically.
Similar concern is illustrated with the third approach, where the cells of interest are expected close to the surface of the sample (within the first 10 microns of the resin block). In that case and for preserving the cells of interest, systematic serial sections are collected and recorded by TEM. The correlation is then performed a posteriori by overlapping the 3D dataset from both imaging modalities. In this work, we show that endogenous landmarks can serve as fiducial for the volume registration. We have used blood vessels bifurcations as well as fluorescent cancer cells to build sets of points that were paired and matched together in Amira. The pairing can only be performed if the landmarks are clearly visible in both imaging modalities. Their detection is made possible in fluorescence microscopy by the use of dyes or of fluorescent proteins, while in EM, they show a straight forward pattern that can be identified unambiguously.
In some instances though, tissues can be too dense or be perturbed by the onset of a large and vascularized tumor, making the vessels recognition difficult in both FM and EM. As illustrated in figure  5 , in such conditions, the density of the tissue as observed by TEM hinders significantly the recognition of the targeted cell. Nevertheless, our approach was versatile enough to allow the use of other structures for the correlation, such as the collagen meshwork, which is again visible in both imaging modalities. Indeed, collagen fibers can be detected from living tissues using SHG, and appear as bundles of electron dense fibers in EM micrographs. The segmentation of these fibers on both datasets allowed a precise registration (after non-linear transformations) and a prediction of the position of the cell of interest.
The use of anatomical features as a basis for correlation can also be applied to studies of other biological questions in a wide range of samples and applications, making it a highly versatile approach. We have demonstrated correlation between FM, LM and EM based on different examples of intrinsic fiducials. Moreover, the approach described here could be extended by recently developed, advanced 3D EM approaches, including Focused Ion Beam Scanning EM  [58]  and serial blockface electron microscopy  [59]. In both methods, numerous cycles of sequentially imaging and removing the sample surface result in voluminous 3D EM datasets. Correlation of the serial-sectioning dataset and the 2PEM model enables identification of the ROI within the sample, which can then be imaged either of these techniques.
With these improved methodologies for correlating dataset from multiphoton microscopy to 3D electron microscopy, we bring an accessible set of solutions to perform structure-function analysis upon intravital imaging of tumor behavior in a murine model. In this paper, we show that mouse intravital imaging of a tumor can be efficiently correlated to the ultrastructural analysis of selected cancer cells. The subcutaneous graft model in the mouse ear has several advantages for studying the behavior of cancer cells in a native microenvironment, as it allows functional imaging over time, the capture of rare events at the single cell level. Moreover, our technique allows the recovery of the same cells for ultrastructural analysis. We therefore expect such methods will allow the study of fundamental features of the metastasis cascade within intact tissues.
G. Overlay of a 3D model of the 2PEM z-stack (based on A, magenta) and a model of the vessels segmented in the serial thick sections (based on F, yellow). Overlapping areas are highlighted in white. Asterisks indicate vessel-forks that are visible in both datasets. Scale bar: 50 mm. See Movie S3. H. Correlation between 2PEM, LM and EM enabled retrieving the cells of interest (numbering in bottom right corner, as in B and C) in the sequence of serial thin sections. The electron transparent areas in proximity of the cells indicate a local absence of extracellular matrix components. The cause of this missing material remains to be elucidated. I. A selected area in the 2PEM dataset, showing a cell of interest (number 4 in B, C and H). J. Selection of the TEM images of cell 4, shown in panel I.
The cell is modeled in green and the vessel lumen in red. For each image, we indicated the z-distance from the section where the cells was first spotted. Scale bar: 10 mm. K. Cell 4 (green) and the blood vessel lumen (red) are segmented in the serial sections (J). A 3D model of this segmentation reveals the shape of the cell, which closely resembles the 2PEM view of the same cell, shown in I. Scale bar: 10 mm. L-N: Electron tomography of a tumor cell (number 2 in B,C, and H), shown at low magnification in panel L. At the position of the box in L, a tomogram was obtained. Scale bar: 5 mm. See Movie S3. M. One selected virtual slice of the tomogram shows the complex architecture of the cell membrane and the fiber-like structure of extracellular matrix (ECM). N: The high image contrast of the ECM fibers enables semi-automatic segmentation of the fibers (shown in blue) within the 3D dataset.
In order to predict the position of the ROI (ball, visible in volume 1) in volume 2, similar points (landmarks) in both datasets are identified ('Correlation'). These landmarks are overlaid in 3D ('Registration') enabling warping of volume 1 into volume 2 and thus also projecting the position of the ROI in volume 2 ('Docking'). C. Collagen fibers can be observed in both the 2PEM z-stack (top row panels, 2PEM) and the EM images of the serial sections (middle row, TEM). In the TEM images, the collagen is visible as electron dense fibers running in between the cellular material. Each column shows a 2PEM and an EM image obtained at a similar position in the sample, pre-and post EM processing. The numbers in the top left corner of the panels indicate their position in z to the image shown in the first column.
The 2PEM dataset is stretched in z relative to the TEM dataset, causing the larger z-steps between the 2PEM images in the different rows. The bottom row shows the overlay of the 2PEM image (blue) and the TEM image (grey levels). Scale bars: 100 mm. D-F. The landmark-registration procedure is performed in Amira, using 3D models of collagen fibers as a reference. The selected landmarks (colored 'targets') indicate corresponding points in both datasets. D. 3D model of the collagen fibers (blue) and the cell of interest (green), obtained from a high magnification 2PEM z-stack of the cell of interest. E: 3D model of the collagen fibers (grey), obtained from a z-stack of EM images. F. The 3D visualization software Amira was used to overlay the landmarks and perform non-linear transformations to the 2PEM model to warp it into the EM model.
G-I: Landmark-registration between the 2PEM and EM datasets enables retrieval and electron tomography of the cell of interest (green). G. Following the registration of the 2PEM in the EM dataset, the cell of interest could be docked within the z-stack of EM images. Scale bar: 10 mm. H. The docked cell of interest is outlined in green on a TEM section of the ROI. The nucleus of the cell is visible. Scale bar: 3 mm. K. Virtual slice through the tomogram obtained in the area that is boxed in I. The collagen fibers in the top left corner are clearly recognizable owing to their striated structure and high electron density. Scale bar: 500 nm. See Movie S4.
G-I: Landmark-registration between the 2PEM and EM datasets enables retrieval and electron tomography of the cell of interest (green). G. Following the registration of the 2PEM in the EM dataset, the cell of interest could be docked within the z-stack of EM images. Scale bar: 10 mm. H. The docked cell of interest is outlined in green on a TEM section of the ROI. The nucleus of the cell is visible. Scale bar: 3 mm. K. Virtual slice through the tomogram obtained in the area that is boxed in I. The collagen fibers in the top left corner are clearly recognizable owing to their striated structure and high electron density. Scale bar: 500 nm. See Movie S4. doi:10. 1371/journal. pone. 0114448. g005.
Acknowledgments We thank  Marc Koch  and  Pascal Kessler , and the  IGBMC imaging platform (Illkirch, France) , for assistance in MP imaging. We are grateful to  Didier Hentsch ,  Serge Taubert  and  Ferat Egilmez  for technical help regarding the mouse ear holder and the microscope-adapted gas anesthesia system. We thank the animal facility at INSERM  U1109  for mouse care. We thank  Guillaume Allio  for assistance in collagen segmentation. We are very much obliged to  Wim Hagen  for his help with the electron microscopy and to  Rachel Mellwig  for sharing her insights and for assisting with electron tomography. Finally, we thank the  Electron Microscopy Core Facility of the EMBL Heidelberg  for valuable feedback and help with the sample preparation and image processing.