This protocol presents a method to perform quantitative, single-cell in situ analysis of protein expression to study lineage specification in mouse preimplantation embryos. The procedures necessary for collection of blastocysts, whole-mount immunofluorescent detection of proteins, imaging of samples on a confocal microscope, and nuclear segmentation and image analysis are described.
This protocol presents a method to perform quantitative, single-cell in situ analyses of protein expression to study lineage specification in mouse preimplantation embryos. The procedures necessary for embryo collection, immunofluorescence, imaging on a confocal microscope, and image segmentation and analysis are described. This method allows quantitation of the expression of multiple nuclear markers and the spatial (XYZ) coordinates of all cells in the embryo. It takes advantage of MINS, an image segmentation software tool specifically developed for the analysis of confocal images of preimplantation embryos and embryonic stem cell (ESC) colonies. MINS carries out unsupervised nuclear segmentation across the X, Y and Z dimensions, and produces information on cell position in three-dimensional space, as well as nuclear fluorescence levels for all channels with minimal user input. While this protocol has been optimized for the analysis of images of preimplantation stage mouse embryos, it can easily be adapted to the analysis of any other samples exhibiting a good signal-to-noise ratio and where high nuclear density poses a hurdle to image segmentation (e.g., expression analysis of embryonic stem cell (ESC) colonies, differentiating cells in culture, embryos of other species or stages, etc.).
The mouse preimplantation embryo is a paradigm to study the emergence and maintenance of pluripotency in vivo, as well as a model for the study of cell fate specification and de novo epithelialization in mammals. The preimplantation stages of mammalian development are dedicated to the establishment of the three cell lineages that make up the blastocyst, namely the pluripotent epiblast – which gives rise to most somatic tissues and germ cells – and two extraembryonic lineages, the trophectoderm (TE) and the primitive endoderm (PrE) (Figure 1A) 1,2. This protocol describes the procedures to (1) harvest and fix preimplantation stage mouse embryos, (2) perform immunofluorescence to label proteins of interest, (3) carry out whole-mount imaging using a confocal microscope with z-sectioning capabilities and (4) perform nuclear segmentation of confocal images and subsequent quantitative image analyses. This pipeline allows the unbiased measurement of protein levels for the assignment of cell identities to characterize subpopulations of cells in situ. This protocol can be carried out in as little as 3 – 4 days for a single litter (generally up to 10 mouse embryos), from embryo collection to the data analysis (Figure 1B). The simultaneous analysis of several litters would increase the imaging and data analysis time burden, thus extending the overall length of the protocol.
The preimplantation stage mouse embryo is an experimentally tractable system which, given its small size and stereotypical morphology 3, is well suited for in toto imaging of cellular processes with single-cell resolution. To carry out an unbiased, systems-level analysis of a statistically relevant number of embryos, an automated, quantitative analysis pipeline is desirable. However, due to the high nuclear density of the inner cell mass (ICM) of the blastocyst (Figure 1A, 2D), conventional image segmentation platforms fail to provide sufficient accuracy to establish an automated or semi-automated workflow. On the other hand, manual segmentation, while accurate, does not allow the processing of large cohorts of cells and embryos, nor is it suitable for a reproducible, unbiased determination of cell identities – which is especially critical when studying developmental stages where patterns of marker expression have not fully resolved (e.g., do not exhibit a binary distribution across a population). We have recently developed and validated an image segmentation method tailored for mouse preimplantation stage embryos and for mouse embryonic stem cells (ESCs) that achieves high accuracy, while requiring minimal user input 4-8.
The analysis pipeline presented here revolves around the MATLAB-based image segmentation tool Modular Interactive Nuclear Segmentation (MINS) 4. MINS performs unsupervised nuclear segmentation on large batches of confocal Z-stacks after the user has established a minimal number of image properties, using a graphical user interface (GUI) (Table 1) 4. This pipeline has proven efficient for the generation of high throughput data on protein expression and cell localization in both wild type, experimentally treated and genetically modified embryos and ESCs 5-7. In the present protocol, we describe the application of MINS to the segmentation of preimplantation-stage embryo images. For examples of MINS performance on ESCs please refer to 4,7. The automated nuclear segmentation step significantly reduces the time burden of the cell identification process, whereas the spatial and fluorescence intensity measurements allow an unbiased determination of cell identities and the generation of three-dimensional maps of gene expression domains and cell position in the embryo (Figure 1C). Moreover, the scalability of this workflow makes it applicable to the analysis of individual litters through large cohorts of experimentally treated embryos, or embryos of different genetic backgrounds 5,6. MINS is freely available at http://katlab-tools.org (the software requires a MATLAB license).
No approach developed to date allows the generation of such in-depth data on protein expression and cell localization in mouse preimplantation embryos. All attempts thus far at quantifying these types of data have been restricted to the manual determination and quantitation of cell numbers for different populations in the embryo (either entirely manually, or software-assisted) 9-19. This approach (incorporating MINS software) has been tailored for and tested on mouse preimplantation embryos and ESCs; nevertheless its performance on other systems with high nuclear density, although yet untested, is expected to be equivalent.
Ethics statement: All animal work, including husbandry, breeding and sacrifice was approved by Memorial Sloan Kettering Cancer Center's Institutional Animal Care and Use Committee (IACUC), protocol #03-12-017.
1. Embryo Collection
Note: All animal work must have been approved by institutional and local authorities and conform to local and institutional rules.
2. Immunofluorescence
3. Confocal Imaging
Note: Individual confocal microscope configurations will require specific acquisition parameters to be adjusted for the system and software in place. However, the following section provides a set of general rules to follow that should be applicable to any given installation.
4. Image Analysis and Data Pre-processing
To facilitate data interpretation and presentation, care should be taken not to damage the embryos during collection and manipulation, so that all cells and their relative position can be analyzed. Figure 2A – D shows examples of intact blastocysts at different stages with an expanded cavity. Should damage occur, extra care should be taken when analyzing and interpreting results.
The quality and reliability of the data generated using this protocol is dependent on the quality of the fixation and on the signal-to-noise ratio of the antibodies used to detect the proteins of interest. Always use fresh fixative and test new antibodies, with appropriate positive and negative controls, before beginning a set of experiments. Figure 2A shows examples of good antibodies for a number of nuclear proteins. Figure 2B shows an example of a good antibody for a cytoplasmic protein (DAB2). Figure 2C shows an example of a bad staining, with low signal-to-noise ratio, where the sample was fixed for only 10 min. In this case, the antibody used for GATA4 (Santa Cruz, sc-1237) requires O/N fixation to provide a strong signal (see 24 for instances of embryos fixed O/N and stained with this antibody). Increasing the signal level during post-processing reveals a very noisy image. By contrast the anti-GATA4 used for Figure 2A (sc-9053) provides high signal-to-noise ratio after only 10-min fixation. Details for these and other robust antibodies are provided in the Materials section.
The limiting factors for a good MINS segmentation are a) the magnification of the objective used for imaging, b) the Z-resolution and c) the quality of the nuclear staining. Figure 2D shows examples of embryos imaged with an oil immersion 40X objective with a NA of 1.30 and a 0.21 mm WD. Middle panels show magnifications of the ICMs where individual nuclei and the border between them can be distinguished. If not using DNA staining (i.e., Hoechst, DAPI, TO-PRO3, YO-YO1, etc.) fluorescence values for quantification, acquisition parameters can be individually adjusted to obtain the best signal. Bottom panels show how the more advanced the embryo, the higher the nuclear density and thus the higher the chance of segmentation errors (arrowhead and arrow). Figure 3 shows examples of errors that MINS can commit, such as detecting apoptotic nuclei as live cells (asterisks in panels in Figure 3Aa, Ab, Ad), over-segmentation (arrow and arrowheads in Figure 3Ac) or under-segmentation (arrow in Figure 3Ad). Figure 3B shows a sequence of Z-slices of an under-segmentation event, where two cells have been identified as one. Note how MINS segmentation takes the Z-axis into account to segment a volume that comprises all slices where a given cell is present.
Finally, the specifics of the data analysis will depend on the question under study, therefore, guidelines for the analysis process cannot be given here. However, we have found certain initial data transformations to be useful. Figure 4B – D shows plots of the fluorescence values for the markers NANOG and GATA6 in the ICM of the embryo shown in Figure 4A. Figure 4B shows the raw fluorescence values obtained from MINS (after manual correction of under- and over-segmentation). Figure 4C shows the same data after a logarithmic transformation, which separates the values from the plot axes (a square root transformation is also possible and yields a similar plot). Figure 4D shows the same data after automatically correcting the values for the Z-associated attenuation in fluorescence, as explained in step 4.5.2.2 of the protocol. Examples of this Z-associated fluorescence decay are given in the image in Figure 4E and the plots in 4F. Figure 4G shows the effect of the data transformation explained in 4.5.2.2. Colors representing either epiblast (red, NANOG+, GATA6-) or PrE (blue, NANOG-, GATA6+) identity have been added to the plot as a function of the NANOG and GATA6 values. In this particular example, cells where the ratio of log[GATA6]/log[NANOG] >1.25 were considered PrE, cells where the ratio of log[GATA6]/log[NANOG] < 1 were considered EPI, and cells with an intermediate ratio were considered uncommitted (none in this case). All data transformations and plots have been done in Rstudio, however, the choice of software is not critical and will depend on the end user.
Figure 1. Embryo Location, Timeline and Analysis Pipeline. (A) Schematic of one (of two) mouse uterine horn and the stages of preimplantation development, aligned with the region of the horn where they should be found. (B) Experimental timeline with timing for each step. Steps of the protocol and pause points (blue) are indicated. (C) Summary of image acquisition and analysis pipeline using MINS. Please click here to view a larger version of this figure.
Figure 2. Examples of Immunofluorescence and Nuclear Density. (A) Examples of good immunofluorescence results with tested antibodies for nuclear transcription factors. Anti-CDX2 was detected with an AlexaFluor 488 donkey anti-mouse secondary antibody, anti-GATA6 with an AlexaFluor 568 donkey anti-goat, anti-NANOG with an AlexaFluor 647 donkey anti-rabbit, anti-GATA4 (Santa Cruz, sc-9053) with an AlexaFluor 488 donkey anti-rabbit and anti-OCT4 with an AlexaFluor 647 donkey anti-mouse. All primary antibodies are listed in the Materials Table. Cytoplasmic GATA4 nuclear staining is non-specific, and appears also in Gata4 null embryos (not shown). (B) Example of good immunofluorescence for a cytoplasmic protein, DAB2. It has been detected using an AlexaFluor 488 donkey anti-mouse. (C) Example of bad immunofluorescence, with a low signal-to-noise ratio for a nuclear factor. GATA4 has been detected with an anti-GATA4 raised in goat (Santa Cruz, sc-1237; whereas sc-9053 (rabbit anti-GATA4) was used in (A)) and an AlexaFluor 568 donkey anti-goat. (D) Examples of intact embryos with good nuclear staining showing how nuclear density increases with embryo age and how this increases errors in MINS segmentation (arrowhead – oversegmentation – and arrow – undersegmentation). Scale = 20 µm. Please click here to view a larger version of this figure.
Figure 3. Examples of MINS Errors. (A) Sequence of individual Z-sections from one embryo showing examples of MINS segmentation errors. Apoptotic cells which are nevertheless identified as nuclei are marked with an asterisk (*) in panels a, b and d. In panel b, ID #39 (arrowhead), albeit very small, does identify the corresponding nucleus and can thus be left uncorrected. In panel c, ID #66 (arrow) spans two different nuclei, which have in turn been identified separately (arrowheads, IDs #65 #54 and #53). In panel d, two cells (ID #63) have been identified as a single one (see arrow marking the thin border) and this record should therefore be duplicated for cell counting purposes. (B) MINS detects cells along the Z-axis. The images show a sequence of Z-slices of two cells that have been erroneously scored as a single one (#123). Note how the blue area delimiting the nuclei is continuous along Z. Please click here to view a larger version of this figure.
Figure 4. Example of Segmentation and Data Transformations. (A) Images of ICM cells of a blastocyst stained for NANOG and GATA6 and snapshot of nuclear segmentation of the same embryo using MINS on the nuclear staining channel (Hoechst 33342). (B-D) NANOG and GATA6 fluorescence values in ICM cells measured by MINS plotted as raw data (B), after performing logarithmic transformation (C) and after correcting values for the fluorescence decay along the Z axis and automatically assigning cell identities based on the corrected values (D). (E – G) Examples of data correction for fluorescence decay along the Z axis. (E) Images of a blastocyst labeled with Hoechst and anti-CDX2+AlexaFluor 488 (AF488), showing fluorescence decrease along the Z axis. (F) Scatter plots of fluorescence values along the Z axis for Hoechst and AF488 for a pool of embryos including the one shown in (E). Gray line represents the regression curve for each set of values. (G) Same data as in F after compensating fluorescence decay for each cell using the following factor: Z coordinate * the slope of the linear model (see step 4.5.2.2 in the protocol). Panels E – G were originally published by the authors in the Node (http://thenode.biologists.com/99problems/discussion/) Each dot represents one cell. Scale = 20µm. Please click here to view a larger version of this figure.
User Input |
Brightness threshold* (facilitates nuclear detection in dim images) |
Relative X/Y-Z resolution |
Channel to segment |
Nuclear diameter |
Image noise level |
MINS Output |
Segmentation Z-stack with nuclei IDs |
Nuclear volume |
XYZ coordinates for each nucleus |
Average and summation of fluorescence values for each fluorescence channel and nucleus |
Advantages |
Accurate segmentation (>85%) of nuclei throughout the stack — recognizes all occurrences of a nucleus in adjacent z-sections |
Single cell resolution |
Unsupervised segmentation of batches of embryos |
Segmentation pipelines can either be saved and re-used or adjusted for each particular embryo |
Table 1. MINS Features
Load Image | At the prompt, introduce the Z relative resolution for the image. It can be obtained from the file metadata using the default reader or applying the following formula: X (or Y) resolution/Z resolution (all in µm). |
Enter the sequence number (1 – 5) for the channel to be segmented. Using the DNA stain channel for segmentation will detect all cells in the image, however, other channels may be segmented separately too. | |
Enter the frame to begin segmentation at (1 by default). Only applies to files with multiple time frames. | |
Enhance Image (optional) | The white and black point of the image can be adjusted to facilitate detection. Generally lowering the white point and re-scaling to 0 – 255 (for 8-bit images) or 0 – 4096 (for 12-bit images) will make the image brighter and facilitate detection of dim nuclei. |
Detect Nuclei | Select the estimated nuclear diameter (generally between 30 and 40 px in blastocysts). |
For noisy images the noise level can be adjusted. Otherwise, the default value will be applied. | |
Segment Nuclei | Use default parameters. |
Classify Nuclei | MINS can distinguish trophectoderm (TE) from inner cell mass (ICM) cells in preimplantation embryos based on position. This can also be done manually or based on fluorescence levels if a TE marker is used. |
Export Results | Select the files to be generated: |
– segmentation overlaid over the channel used (.tiff sequence file), | |
– segmentation only (.tiff sequence file), | |
– spreadsheet with IDs for all cells detected, XYZ coordinates and fluorescence levels (average and sum) for all channels for each cell (.csv file). | |
All files exported by default. Files are saved in the same directory where images are located. |
Table 2. MINS Segmentation Pipeline
The present protocol describes a method to perform a quantitative analysis of whole-mount immunofluorescence on preimplantation stage mouse embryos. A robust immunofluorescence protocol 22 is followed by high-resolution, whole-mount confocal imaging and by image segmentation using a tailored piece of software 4. While the choice of immunofluorescence protocol is not critical, we find the one presented here 22 to be fast, reliable and to provide robust signal for many of the antibodies we have tested. Other protocols may be followed, provided their application is consistent across equivalent experiments. While many factors affect the outcome of each individual experiment, and direct comparison between experiments is not necessarily possible, we have found that striving for consistency across repetitions and performing sufficient technical replicates greatly reduces variability. Therefore, once optimized, fixation and immunolabeling reagents and protocols, as well as imaging conditions should be kept constant between equivalent experiments.
The main problems that may arise during the application of this protocol fall into two groups: 1) poor quality staining, with dim signal, and 2) suboptimal segmentation, with many errors (i.e., over- and under-segmentation). Low quality immunofluorescence is generally due to improper fixation of the samples or to the antibodies not being suitable for whole-mount immunofluorescence. Ensure PFA is fresh (either freshly prepared or thawed to RT from -20 ºC no longer than a week prior). Given the small size of preimplantation embryos, 10-min fixation in PFA should suffice. However, we have found certain antibodies to yield a stronger signal with longer fixation times (see Materials). If an antibody that has been previously tested provides weak signal, try different fixation protocols. Not all antibodies perform robustly on whole-mount immunofluorescence and the choice of primary antibody is critical for the outcome of the experiment. New antibodies should be tested on embryos and the optimal concentration determined empirically. In the Materials chart we provide details of a number antibodies we have tested and use routinely. Refer to the published literature or to the manufacturer's information when considering new antibodies. Note that not all antibodies suitable for Western blot work in whole-mount immunofluorescence, and the concentrations necessary for immunofluorescence may be up to one order of magnitude higher. Commercial secondary antibodies generally work well out of the box, however, be consistent in the use of primary-secondary antibody combinations throughout equivalent experiments that are going to be compared.
The quality of image segmentation depends on both the quality of the nuclear staining and on the nuclear density in the ICM of the embryo. Always strive for bright, sharp nuclei and use a high-resolution objective (40X or more). If the nuclear stain signal is low, use a fresh aliquot or a new batch. As long as the nuclear stain is not used for quantification, the acquisition parameters can be adjusted for each embryo in order to record sharp, bright nuclei. Likewise, if a channel other than the nuclear staining is used for segmentation, the signal-to-noise ratio of that particular channel will determine the quality of the segmentation. Late stage blastocysts (E4.0 onwards) present a very high nuclear density within the ICM. Therefore, it is at these stages that high quality staining is most critical. Nevertheless, segmentation errors will take place most often at these stages. Introduce the estimated nuclear diameter and the image noise level on the MINS pipeline, explore the outcome of each step using the 'View' button and adjust the parameters until a satisfactory result is obtained. Use the parameters that produce the best segmentation and manually correct errors during data processing. Note that late stage embryos (~E4.5) have a high cell count and manual data correction will be time consuming.
The limitations of this protocol are determined by the confocal microscope system used for image acquisition. As discussed, use a high-magnification objective, with a working distance that allows Z-axis imaging of the entire embryo (preimplantation mouse embryos can reach up to 150 – 160 µm in diameter). Similarly, the number of epitopes that can be detected will be determined by the number of channels the microscope can acquire at once. Using this protocol, three different epitopes besides the DNA (4 channels in total) can be labeled simultaneously on each sample. Ensure the instrument is calibrated for each fluorescence channel, the gating is optimized and that the laser output is consistent.
The methods described here allow the analysis of cell position along the XYZ axes as well as the quantification of protein expression levels in situ for every single cell in mouse blastocysts. In our experience, alternative methods using any other available software cannot provide the level of resolution that the pipeline applied in this protocol can (see 4 for a direct comparison with other tools). The high nuclear density found in the ICM of the blastocyst causes other tools to generate so many mistakes that the extent of manual correction required makes their use impractical. Alternatively, manual cell counting and fluorescence measurements have been previously used for subsets of cells or defined regions of the embryo 10,11,18,19,24. However, manual counting and measurement of all fluorescence channels and cell position, across all XYZ axes, for the tens of cells present in each embryo would be so time consuming that it would not allow for the high-throughput analysis for which our protocol was devised. Moreover, manual assessment of fluorescence levels is prone to bias, while a pipeline such as the one described here allows an objective measurement of fluorescence intensities for the subsequent determination of cell identity. In order to assign cell identities, the investigator should establish a standard criterion to be applied across all samples for a particular experimental set up. Given the different factors – biological and technical – that can affect the outcome of immunolabeling, this criterion should be determined using appropriate control experiments and previously published data wherever possible. We envision a near future where an algorithm may be designed for automatic determination of cell identity regardless of the experiment. However, such a tool will only come from more extensive application and improvement of the current method, most likely coupled with machine learning algorithms.
Finally, given the simplicity of this pipeline and the stereotypical morphology of mouse preimplantation embryos, this protocol is easily scalable for the analysis of large numbers of embryos, thus allowing high-throughput analyses of null phenotypes 5,6 or any other experimental manipulation 7. Finally, while the current protocol has been designed and optimized for the analysis of preimplantation mouse embryos and ESC colonies, we see no reason a priori why it could not be applied to other systems with similar characteristics – namely high nuclear density. We encourage others to experiment and adapt this protocol to other scenarios and provide feedback for its future improvement.
The authors have nothing to disclose.
The authors would like to thank Stefano Di Talia, Alberto Puliafito, Venkatraman Seshan and Panagiotis Xenopoulos, for input on data handling, analysis and representation, Berenika Plusa for assistance in the design of the immunofluorescence protocol and antibody testing and members of the Hadjantonakis lab for comments on the manuscript and on the development of this protocol. Work in our lab is supported by the National Institutes of Health R01-HD052115 and R01-DK084391, and by NYSTEM N13G-236.
Embryo collection | |||
Blunt probe for plug checking | Roboz | RS-9580 | |
Forceps | Roboz | RS-4978 | |
Surgery scissors | Roboz | RS-5910 | |
Glass Pasteur pipettes | Fisher | 13-678-20C | |
Pre-assembled aspirator tube & mouthpiece | Sigma | A5177 | |
Longer rubber tubing | Fisher | 14-178-2AA | |
M2 | Millipore | MR-015-D | |
FHM | Millipore | MR-024-D | |
Acid Tyrode's solution | Millipore | MR-004-D | |
Penicillin/Streptomycin | Gibco | 15140 | |
Bovine Serum Albumin | Sigma | A9647 | |
4-well plates | Nunc/Thermo-Fisher | 12-566-300 | |
Name | Company | Catalog Number | Yorumlar |
Immunofluorescence | |||
96-well U-bottom plates | Fisher | 14-245-73 | |
Triton X-100 | Sigma | T8787 | |
Glycine | Sigma | G7403 | |
Horse serum | Sigma | H0146 | |
Primary antibodies | Concentration | ||
CDX2 | Biogenex | AM392-5M | 1:200 |
GATA6 | R&D | AF1700 | 1:100 |
GATA4 | Santa Cruz | sc-9053 | 1:100, 10 min fixation |
GATA4 | Santa Cruz | sc-1237 | 1:100, overnight fixation |
SOX17 | R&D | AF1924 | 1:100 |
Nanog | ReproCELL | RCAB0002P-F | 1:500 |
OCT4 | Santa Cruz | sc-5279 | 1:100, 10 min to overnight fixation |
DAB2 | BD | BD-610464 | 1:200 |
Secondary antibodies | Life Technologies | Various | 1:500 |
Hoechst 33342 | Life Technologies | H3570 | |
Name | Company | Catalog Number | Yorumlar |
Imaging | |||
35 mm glass-bottom dishes | MatTek | P35G-1.5-14-C | |
Name | Company | Catalog Number | Yorumlar |
Segmentation | |||
Computer running 64-bit Windows OS | n/a | Verify minimal system requirements at http://katlab-tools.org and in Lou et al., (2014) Stem Cell Reports | |
MATLAB (software) | Mathworks | n/a | |
MINS (software) | Free | n/a | http://katlab-tools.org |