We acknowledge the support staff at the UCDNA Services Facility, as well as the Animal Care facility staff at the University of Calgary. We thank Matt Workentine for his bioinformatics support and Jens Durruthy for his technical support. This work was funded by a CIHR grant (R.M. and J.B.), a CIHR New Investigator Award to J.B., and an Alberta Children&#39;s Health Research Institute Fellowship (J.S.).

<ol>
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Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+parallel+genome-wide+expression+profiling+of+individual+cells+using+nanoliter+droplets.">Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.</a> Cell. 161, 1202-1214 (2015).</li><li>Zappia, L., Phipson, B., Oshlack, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+the+single-cell+RNA-seq+analysis+landscape+with+the+scRNA-tools+database.">Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database.</a> bioRxiv:206573. , (2018).</li><li>Dulken, B. W., Leeman, D. S., Boutet, S. 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Sequencing Requirements for Single Cell 3' Available from: <a target="_blank" href="https://support.10xgenomics.com/single-cell-gene-expression/sequencing/doc/specifications-sequencing-requirements-for-single-cell-3">https://support.10xgenomics.com/single-cell-gene-expression/sequencing/doc/specifications-sequencing-requirements-for-single-cell-3</a> (2018)</li><li>. Introduction to R Available from: <a target="_blank" href="https://www.datacamp.com/courses/free-introduction-to-r">https://www.datacamp.com/courses/free-introduction-to-r</a> (2018)</li><li>. Droplet-based, high-throughput single cell transcriptional analysis of adult mouse tissue using 10X Genomics#39; Chromium Single Cell 3' (v2) system: From tissue preparation to bioinformatic analysis Available from: <a target="_blank" href="https://figshare.com/s/97b83e649e5eefd01357">https://figshare.com/s/97b83e649e5eefd01357</a> (2018)</li><li>. 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No disclosures

This protocol demonstrates how the appropriate preparation of single cells can uncover the transcriptional heterogeneity of thousands of single cells and discriminate functional states or unique cellular identities within a tissue. The protocol does not require fluorescent reporter proteins or transgenic tools and can be applied to the isolation of single cells from various tissues of interest including those from humans; keeping in mind&#160;each tissue is unique and this protocol will require some degree of adjustment/modification.
The diverse and highly dynamic transcriptional programs within cells have emphasized the value of single-cell genomics. Aside from isolating high-quality RNA, a critical sample preparation step necessary for high quality datasets is ensuring that cells are completely released from tissue and that cells are healthy and intact. This is relatively straight forward for collecting cells that are easily released, such as circulating cells or in tissues where cells are loosely retained, such as in lymphoid tissues. But this can be challenging for other adult tissues, due to the highly-developed cellular architecture spanning large distances, surrounding extracellular matrix and the often-rigid cytoskeletal proteins involved in maintaining cell structure. Even with appropriate dissociation techniques for the full release of cells, there is potential that the rigorous and often lengthy processing required would alter mRNA quality and cell integrity. In addition, the high temperatures used for enzyme-assisted dissociation also affect transcriptional signatures29,30. The intent of the protocol is to present quality control checks, using tissues such as the myelinated adult nerve and the extracellular matrix-rich adult skin, to demonstrate how&#160;optimization can help to overcome these obstacles.
A major consideration when designing any scRNA-Seq experiment is the choice of sequencing depth. Sequencing can be highly multiplexed and read depth can vary from being very low using Drop-Seq2 to up to 5 million reads/cell14 using a full-length RNA-Seq method such as Smart-Seq. Most scRNA-Seq experiments can detect moderate-to-high expression transcripts with sequencing as low as 10,000 reads/cell, which is usually sufficient for cell type classification41,42. Shallow sequencing depth is of value to save on sequencing costs when trying to detect rare cell populations across complex tissues where thousands of cells may be needed to confidently ascribe rare populations. But shallow depth sequencing is not adequate when detailed information on gene expression and processes associated with subtle transcriptional signatures is necessary. Currently, it is estimated that the large majority of genes in a cell are detected with 500,000 reads/cell, but this can vary depending on the protocol and tissue type43,44. While full-length transcript sequencing circumvents the need for assembly and can, therefore, detect novel or rare splice variants, sequencing costs often limit scaling such approaches to examine thousands of cells comprising a complex tissue system. In contrast, 3&#39; tagged single-cell libraries such as the ones described in this protocol typically have lower complexity and require shallower sequencing. It is important to note that libraries generated using the described protocol can be sequenced on one of five supported sequencers: 1) NovaSeq, 2) HiSeq 3000/4000, 3) HiSeq 2500 Rapid Run and High Output, 4) NextSeq 500/550, and 5) MiSeq.
An alternative approach to single cell RNA-Seq, that reduces the need for delicate tissue&#160;and cell handling yet maintains some of the benefits of single cell RNA-Seq, is the analysis of RNA from single nuclei45. This approach&#160;allows more rapid processing reducing RNA degradation, and more extreme measures to ensure adequate release of nuclei, and thus likely allows for a more confident capture of the transcriptional profiles representing all cells within a given tissue. This would, of course, only provide a portion of the transcriptional activity present within a given cell, thus depending on what experimental objectives are of interest this approach may or may not be appropriate.
Besides the complete characterization of cellular identities within a given tissue, one of the most valuable analyses for&#160;scRNA-Seq datasets is the assessment of intermediate transcriptional states across &#39;defined&#39; cell populations. These intermediary states can impart insights into the lineage relationships between cells within identified populations, which was&#160;not possible with traditional bulk RNA-Seq approaches. Several scRNA-Seq bioinformatic tools have now been developed to elucidate this. Such tools can assess the processes involved in, for example, cancer cells transitioning to an oncogenic/metastatic state, stem cells maturing into diverse terminal fates or immune cells shuttling between active and quiescent states. Subtle transcriptome differences in cells may also be indicative of lineage biases that, recently developed bioinformatic tools like FateID, can infer47. Since the distinctions between transitioning cells can be difficult to ascertain given the transcriptional differences may be subtle, deeper sequencing may be necessary46. Fortunately, coverage of a shallowly sequenced library can be increased if interested in probing the dataset further by re-running the library on another flow cell.
Taken together, this protocol provides an easy-to-adapt workflow that enables users to transcriptionally profile hundreds to thousands of single-cells within one experiment. The final quality of a scRNA-Seq dataset relies on optimized cell isolation, flow cytometry, cDNA library generation, and interpretation of raw gene-barcode matrices. To this end, this protocol provides a comprehensive overview of all key steps that can be easily modified to enable studies of diverse tissue types.

With the development of high throughput single cell technology1,2 and advancements in user-friendly bioinformatics tools over the last decade3, a new field of high-resolution gene expression analysis has emerged &#8211; single-cell RNA sequencing (scRNA-Seq). The study of single cell gene expression was first developed to identify heterogeneity within defined cell populations, such as in stem cells or cancer cells, or to identify rare populations of cells4,5, which were unattainable using traditional bulk RNA sequencing techniques. Bioinformatic tools have enabled the identification of novel sub-populations (Seurat)2, visualization of the order of cells along a psuedotime space (Monocle)6, definition of active signaling networks within or between populations (SCENIC)7,&#160;prediction of the assembly of single-cells in an artificial 3D space (Seurat, and more)8. With these new and exciting analyses available to the scientific community, scRNA-Seq is fast-becoming the new standard approach for gene expression analysis.
Despite the vast potential of scRNA-Seq, the technical skillsets required to produce a clean dataset and to accurately interpret results can be challenging to newcomers. Here, a basic, but comprehensive protocol, starting from the isolation of single cells from whole primary tissues to visualization and presentation of data for publication is presented (Figure 1). First, the isolation of healthy single cells can be deemed challenging, as different tissues vary in their degree of sensitivity to enzymatic digestion and subsequent mechanical dissociation. This protocol provides guidance in these isolation steps and identifies important quality control checkpoints throughout the process. Second, understanding the compatibility and requirements between single cell technology and next-generation sequencing can be confusing. This protocol provides guidelines to implement a user-friendly, droplet-based single-cell barcoding platform and perform sequencing. Finally, computer programming is an important prerequisite for analyzing single-cell transcriptomic datasets. This protocol provides resources for getting started with the R programming language and provides guidance on implementing two popular scRNA-Seq-specific R packages. Together, this protocol can guide newcomers in performing scRNA-Seq analysis for obtaining clear, interpretable results. This protocol can be adjusted to most tissues in the mouse, and importantly could be modified for use with other organisms, including human tissue. Adjustments depending on the tissue and user will be required.
There are several considerations to keep in mind while following this protocol; including, 1) Following all quality control guidelines in Steps 1 and 2 of this protocol is recommended to ensure a viable single cell suspension of all cells within the sample of interest while ensuring accurate total cell number counts (summarized in Figure 2). Once this is achieved, and if all the optimized conditions are followed, the quality control steps can be dropped (to save time - preserving RNA quality and reducing cell loss). Confirming successful isolation of high viability single cells from the tissue of interest is highly recommend before any downstream processing. 2) Since some cell types are more sensitive than others to stress, excessive dissociation techniques can inadvertently bias the population, therefore confounding downstream analysis. Gentle dissociation without unnecessary cellular shearing and digestion is critical for achieving high cellular yields and an accurate representation of tissue composition. Shear forces occur during the trituration, FACS and resuspension steps. 3) As with any RNA work, is it best to introduce as little additional RNase into the sample as possible during preparation. This will help maintain high-quality RNA. Use ribonuclease inhibitor solutions with rinsing to clean tools and any equipment that is not RNase-free but avoid DEPC-treated products. 4) Perform preparations as quickly as possible. This will help maintain high-quality RNA and reduce cell death. Depending on the tissue dissection length and animal number, consider starting multiple dissections/preparations at the same time. 5) Prepare cells on ice when possible to maintain high quality RNA, reduce cell death, and slow cell signaling and transcriptional activity. Albeit, ice-cold processing is ideal for most cell types, some cell types (e.g., neutrophils) perform better when processed at room temperature. 6) Avoid calcium, magnesium, EDTA, and DEPC-treated products during cell preparation.

<table>
	<tbody>
		<tr>
			<td>Products</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>RNAse out</td>
			<td>Biosciences</td>
			<td>786-70</td>
			<td></td>
		</tr>
		<tr>
			<td>Pentobarbital sodium</td>
			<td>Euthanyl</td>
			<td></td>
			<td>50mg/kg</td>
		</tr>
		<tr>
			<td>HBSS</td>
			<td>Gibco</td>
			<td>14175-095</td>
			<td></td>
		</tr>
		<tr>
			<td>Dispase 5U/ml</td>
			<td>StemCell Technologies</td>
			<td>7913</td>
			<td>5 mg/ml</td>
		</tr>
		<tr>
			<td>Collagenase-4 125 CDU/mg</td>
			<td>Sigma-Aldrich</td>
			<td>C5138</td>
			<td>2 mg/ml</td>
		</tr>
		<tr>
			<td>DNAse</td>
			<td>Sigma-Aldrich</td>
			<td>DN25</td>
			<td>10mg/ml</td>
		</tr>
		<tr>
			<td>BSA</td>
			<td>Sigma-Aldrich</td>
			<td>A7906</td>
			<td></td>
		</tr>
		<tr>
			<td>15 ml Narrow bottom tube VWR&#174; High-Performance Centrifuge Tubes</td>
			<td>VWR</td>
			<td>89039-666</td>
			<td></td>
		</tr>
		<tr>
			<td>Sytox Orange Viability Dye</td>
			<td>Molecular Probes</td>
			<td>11320972</td>
			<td>1.3 nM/&#181;l</td>
		</tr>
		<tr>
			<td>Nuc Blue Live ReadyProbes</td>
			<td>Invitrogen</td>
			<td>R37605</td>
			<td></td>
		</tr>
		<tr>
			<td>Agilent Bioanalyzer High senitivity DNA Reagents</td>
			<td>Agilent</td>
			<td>5067-4626</td>
			<td></td>
		</tr>
		<tr>
			<td>Kapa DNA Quantification Kit</td>
			<td>Kapa Biosystems</td>
			<td>KK4844</td>
			<td></td>
		</tr>
		<tr>
			<td>Equipment</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>BD FACSAria III</td>
			<td>BD Biosciences</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Agilent Bioanalyzer Platform</td>
			<td>Agilent</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Illumina&#174; HiSeq 4000</td>
			<td>Illumina</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Illumina&#174; MiSeq SR50</td>
			<td>Illumina</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Software</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>The Cell Ranger</td>
			<td>10x GENOMICS</td>
			<td>support.10xgenomics.com/single-cell-gene-expression</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>/software/overview/welcome</td>
			<td></td>
		</tr>
		<tr>
			<td>Loupe Cell Browser</td>
			<td>10x GENOMICS</td>
			<td>support.10xgenomics.com/single-cell-gene-expression</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>/software/downloads/latest</td>
			<td></td>
		</tr>
		<tr>
			<td>R</td>
			<td></td>
			<td>https://anaconda.org/r/r</td>
			<td></td>
		</tr>
	</tbody>
</table>

droplet barcoding-based single cell transcriptomics of adult mammalian tissues

The analysis of single cell gene expression across thousands of individual cells within a tissue or microenvironment is a valuable tool for identifying cell composition, discrimination of functional states, and molecular pathways underlying observed tissue functions and animal behaviors. However, the isolation of intact, healthy single cells from adult mammalian tissues for subsequent downstream single cell molecular analysis can be challenging. This protocol describes the general processes and quality control checks necessary to obtain high-quality adult single cell preparations from the nervous system or skin that enabled subsequent unbiased single cell RNA sequencing and analysis. Guidelines for downstream bioinformatic analysis are also provided.

The repertoire of open source packages designed to analyze scRNA-Seq datasets has increased dramatically40&#160;with the majority of these packages use&#160;R-based languages3. Here, representative results using two of these packages are presented: assessing unsupervised grouping of single cells based on gene expression, and ordering single cells along a trajectory in order to resolve cell heterogeneity and deconstruct biological processes.
Figure 4 illustrates the use of Seurat for pre-processing quality checks and downstream bioinformatics analysis. First, filtration and removal of deviant cells from analysis is essential for quality checking. This was done using violin (Figure 4a) and scatter plots (Figure 4b) to visualize the percentage of mitochondrial genes, number of genes (nGene), and number of UMI (nUMI) to identify cell doublets and outliers. Any cell with a clear outlier number of genes, UMI, or percentage of mitochondrial genes was removed using Seurat&#39;s FilterCells function. Since Seurat uses principal component (PC) analysis scores to clusters cells, determining statistically significant PCs to include is a critical step. Elbow plots (Figure 4c) were used for PC selection, in which PCs beyond the plateau of the &#39;standard deviation of PC&#39; axis were excluded. The resolution of clustering was also manipulated demonstrating that the number of clusters can be changed, ranging from 0.4 (low resolution leading to fewer cell clusters, Figure 4d) to 4 (high resolution leading to higher cell clusters, Figure 4e). At low resolution, it is likely that each cluster represents a defined cell type, whereas at high resolution this may also represent subtypes or transitional states of a cell population. In this instance, low-resolution cluster settings were used for further analyzing expression heatmaps (using Seurat&#39;s DoHeatmap function) to identify the most highly expressed genes in a given cluster (Figure 4f). In this instance, the most highly expressed genes were identified by assessing differential expression in a given cluster versus all other clusters combined, demonstrating that each cluster was uniquely represented by defined genes. Additionally, individual candidate genes can be visualized on tSNE plots using Seurat&#39;s FeaturePlot function (Figure 4g). This allowed for deciphering whether there were clusters that represented macrophages. Using FeaturePlot, we found that both cluster 2 and 4 were expressing Cd68 - a pan-macrophage marker.
The Monocle package was used for corroborating cell clusters identified in Seurat, and for building cell trajectories, or pseudotemporal ordering, to recapitulate biological processes (Figure 5). Pseudotemporal ordering can be used for samples where single-cell expression profiles are expected to follow a biological time course. Cells can be ordered along a pseudotemporal continuum to resolve intermediate states, bifurcation points of two alternative cell fates, and identify gene signatures underlying acquisition of each fate. Firstly, similar to Seurat&#39;s filtration, poor-quality cells were removed such that the distribution of mRNA across all cells was log normal and fell between upper and lower bounds as identified in Figure 5a. Then, using Monocle&#39;s newCellTypeHierarchy function, single cells were classified and counted using known lineage marker genes (Figure 5b, 5c). For example, cells expressing PDGF receptor alpha or Fibroblast Specific Protein 1 were assigned to Cell Type #1 to create a criterion for defining fibroblasts. Next, this population (Cell Type #1) was assessed to decipher fibroblast trajectories. To do this, Monocle&#39;s differential GeneTest function was utilized, which compared the cells representing the extreme states within the population and found differential genes for ordering the remaining cells in the population (Figure 5d). By applying manifold learning methods (a type of non-linear dimensionality reduction) across all cells, a coordinate along a pseudotemporal path was assigned. This trajectory was then visualized by cell state (Figure 5e) and pseudotime (Figure 5f).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58709/58709fig1v3.jpg" /> 
Figure 1: Flow chart. Steps from whole animal preparation to analyzing single cell RNA-Seq datasets to submitting final datasets to a publicly available repository. Gel beads in Emulsion (GEMs) refer to beads with barcoded oligonucleotides which encapsulate thousands of single cells.&#160;<a href="https://www.jove.com/files/ftp_upload/58709/58709fig1v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58709/58709fig2v3.jpg" /> 
Figure 2: Creating viable single cell suspension from nerve tissue. (a) Cartoon overview of quality control checks. (b) Cells and debris with cells still incorporated in debris (red arrows). (c) Cells released from debris (red arrows). (d) Cell isolation by FACS. P0: debris fraction; P1: cell-like fraction; P3: exclusion of duplets; P4: viability dye (Sytox Orange) negative fraction. (e) No viability dye control. (f) Image of P0 fraction representing isolated debris. (g) Image of P4 fraction representing isolated viable cells (red arrows).&#160;(b)(c)(f) and (g) had nuclear dye added 20 minutes before imaging. Scale Bars: 80 &#181;m. <a href="https://www.jove.com/files/ftp_upload/58709/58709fig2v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58709/58709fig3v3.jpg" /> 
Figure 3: Shallow sequencing predicts the number of recovered cells in 10X processed samples. (a) An example (Sample 1.6) of MiSeq-generated csv listing cell barcodes and its corresponding UMI counts as determined by confidently mapped reads. (b) Barcode rank plot for Sample 1.6 shows one significant drop in UMI count as a function of cell barcodes. The dashed and solid lines represent the cutoff between cells and background as determined by visual inspection. (c) Cell barcodes observed&#160;using the Cell Ranger pipeline post-HiSeq reveals shallow sequencing accurately approximated the number of cells for Sample 1.6. (d) An example of a flow-cell set-up based on shallow sequencing derived cell estimates. For Sample 1.6, since shallow sequencing predicted 3480 cells, 1.17 lanes were assigned to ensure &#62;100,000 reads per cell sequencing coverage in HiSeq. Note: All lanes must add to 100%. <a href="https://www.jove.com/files/ftp_upload/58709/58709fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58709/58709fig4v3.jpg" /> 
Figure 4: Quality control and bioinformatics of single-cell RNA-Seq dataset using Seurat R package. (a) Plots of quality control metrics which include number of genes, number of unique molecular identifiers (UMIs), and the percentage of transcripts mapping to the mitochondrial genome. (b) Sample gene plots detecting cells with deviant levels of mitochondrial transcripts and UMIs. (c) Sample elbow plot used for ad hoc determination of statistically significant PCs. The dashed and dot-dashed lines represent the cutoff where a clear &#34;elbow&#34; becomes apparent in the graph. PC dimensions before this elbow are&#160;included in downstream analysis. (d, e) Graph-based cell clusters visualized at two different resolutions in a low-dimensional space using a tSNE plot. (f) Top marker genes (yellow) for each cluster visualized on an expression heatmap using Seurat&#39;s DoHeatmap function. (g) Visualizing marker expression of, for example, Cd68 gene representing macrophages (purple) using Seurat&#39;s FeaturePlot function. This suggests that cluster 2 and 4 (in panel d) of this dataset represents macrophages. <a href="https://www.jove.com/files/ftp_upload/58709/58709fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58709/58709fig5v3.jpg" /> 
Figure 5: Cell categorization and ordering along peudotemporal trajectory using Monocle toolkit. (a) Inspecting the distribution of mRNA (inferred from UMI counts) across all cells in a sample. Only cells with mRNA between 0 - ~20,000 were used for downstream analysis. (b, c) Assigning and counting cell types based on known lineage cell markers. For example, cells expressing PDGF receptor alpha or Fibroblast Specific Protein 1 were assigned to Cell Type #1 representing pan-fibroblasts using Monocle&#39;s newCellTypeHierarchy function. Number of different cell types can be visualized as a pie chart (b) and as a table (c). (d) Using Cell Type #1 (fibroblasts) as an example, the genes used for ordering cells can be visualized using a scatter plot that demonstrates gene dispersion vs. mean expression. The red curve shows the cutoff for genes used for ordering calculated by the mean-variance model using Monocle&#39;s estimateDispersions function. Genes that meet this cutoff were used for downstream pseudotime ordering. (e, f) Visualization of cell trajectories in a reduced two-dimensional space colored by cell&#39;s &#34;State&#34; (e), and by Monocle-assigned &#34;Pseudotime&#34; (f). <a href="https://www.jove.com/files/ftp_upload/58709/58709fig5v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues at JoVE.com

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

This protocol describes the general processes and quality control checks necessary for preparing healthy adult mammalian single cells for droplet-based, high throughput single cell RNA-Seq preparations. Sequencing parameters, read alignment, and downstream single-cell bioinformatic analysis are also provided.

The analysis of single cell gene expression across thousands of individual cells within a tissue or microenvironment is a valuable tool for identifying ...

droplet-barcoding-based-single-cell-transcriptomics-adult-mammalian

Research

JoVE Journal

Biology

18.4K Views.  University of Calgary. This protocol describes the general processes and quality control checks necessary for preparing healthy adult mammalian single cells for droplet-based, high throughput single cell RNA-Seq preparations. Sequencing parameters, read alignment, and downstream single-cell bioinformatic analysis are also provided.

Video: Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Long-term Imaging Mammalian Cells using Wide-Field Microscopy

Actually what I'm gonna do, mount it in here first and then swap out You. So now I'm gonna aspirate try and be as gentle as possible.Okay. A few seconds just holding.Okay.Alright.So the important features of our lifestyle imaging grid are that, so it's an inverted scope, which means the objective, the samples here in the objective is underneath. So the objective is pointing up and the whole scope, or practically the whole, practically the whole scope, including the objectives and the sample are at 37 degrees. So everything in this plastic box is at 37 degrees.And right on top of the stage we have an air and CO2 mix. That is 5%CO2. So the idea is that the sample is actually in a cell, it's like a cell incubator.And so we have, this is our mixer for the air and the CO2 and, and it gets humidified and heated and then piped onto the, the sample on the stage.Okay. And today we're just gonna be doing fluorescence one channel GFP fluorescence. This is the hose that the CO2 and 5%CO2 goes in and it just fits into the stage holder.That's important. And then we can close these, take these down, and then we go to our first position. And what I'm gonna do is go to the first field, which is my control sample, and I'll pick positions based on what I see on the screen.So I just make sure I actually look into the microscope in each new, well, because the focal plane for each of the wells is different, the, the dish is not perfectly flat on the bottom. So you will need to make fine adjustments when you move from well to the well. So right now I'm on the first, well, what I'll do is I'll focus, so I just hit, I just told, I just turned on the light, the transmitted light for the microscope.And I have make sure all the light is going to the eye pieces. And so I just look in, make sure I get a nice clean field. And then what I'll do is turn that light off, turn the fluorescence light on, make sure the cells that I'm looking at look healthy and they're not apoptotic.And, and I turn that off. And what I do next is send, so now I'm gonna start picking positions, which means I have to te send the light path to the camera. This is the camera right here.So I tell the microscope to send all the camera, they don't see anything in the eye pieces. What I'll do is in the software controlling the microscope, I'll choose the right filter set, which for our purposes is psy. And then set an exposure time.And I know these cells very well and I know that they take a 0.2 second exposure time. So I tell, that's what I tell the software to do. And what I'll do is hit focus.This is, and it's basically just alive. The, A light will turn on and you can, oh, I don't know if you can see cells, but the reason this is a good field is because there's, all the cells are, even in terms of their fluorescence, There are no apoptotic cells. Apoptotic cells tend to be very bright and there's no dirt or schmutz in the field.And you can see that they're actually, this is a metaphase cell and that's a metaphase cell, that's a cell that's probably just exited mitosis. And I can just tell that because it looks like those are, those two sets of chromatin are very nicely oriented to each other. Like they just exited an face.So that's a good field. So, and now that I know that the exposure time and the filter set is correct, and this is gonna be software specific, I'll go and I'll take positions and now the software has memorized the X, Y, Z position of that field. And then when I do pick another field, it's, and I'll, so when I'm picking, when I'm picking new fields, but in the same, well, I'll use the, I'll move the i'll, I'll turn the shutter, I'll open the shutter and I'll look to see what I see on the screen and I'll pick fields based on that versus looking in the field.So I don't see any of my tonics in there. But again, even nicely centered, these cells will go through my PS I continue, here's a good example. Here's a good example of what I would avoid that, see how bright that is?That's very bright. That's probably remnants of an apoptotic cell. And the reason I would avoid that is because since it's so bright, when the software auto focuses, it will autofocus just on that and it won't be in the same plane as all these other cells.And so I won't get any information from that field. That looks nice. There's a, this is a pro metaphase right here, That's A metaphase.So, and I'm gonna take three positions per well. So that was three positions and now I'm going to go to my next Well, so you can, so what I will probably do is take images every 10 minutes. So it's a compromise.Lifestyle imaging is always a compromise, right? You have to image, you have to keep the cells healthy, especially if you're interested in mitosis. So you have to minimize their exposure to the fluorescent line.And, but you wanna get information. So you have to take images, you have to, so you have to compromise, you have to make a choice about what you want to capture, what kind of information you wanna capture. For our purposes, mitosis tends to take about an hour.So if we're taking 10 minute intervals, that's pretty good. We see prophase, we see metaphase, we see anaphase, and we can, we can see when there are, there are treatments or drugs or siRNAs that affect those specific cell cycle stages. So for our purposes, that's enough, but we always want more.We always want more positions or finer time and interval or something like that. So it's a compromise. So, and then I'll hit start.And so our software, it does an autofocusing pass on the first pass. So once I hit start, it should start auto autofocusing and you'll see the shutter open, open and close between auto focusing. So I'll hit start.So that's the shutter opening and closing. The Z motor is engaging and it's actually trying to find the plane with the most contrast, which will be the most in focus. You can actually see on the screen going in and out of finding the best plane of focus that might be worrisome.So what it does is it, it drives the Z motor to find the best, the most in focus plane focus, well the most in image. And then it acquires picture there and then it learns that Z position. And then we'll use that Z position for the next 12 passes.And then we'll do the auto focusing pass again. What Kind of experiments do they do? Okay, so our lab is mostly interested in understanding what regulates mitic progression, or at least I'm most interested in understanding that.So with live cell imaging and especially our multi position, long-term imaging scope, you can take, you can film cells for two or three days entering anding mitosis, and you can image multiple rounds of mitosis so you can understand. And then you can put drugs or RNAs or any kind of perturbations on the cells that you would like to study. So you can understand how, how those drugs or, or genes are, are important in regulating mitotic progression.Because it's not fixed cell work and live cell work, you can understand what, when you need to have a perturbation to affect mitosis or what stage of mitosis is affected. So it's really powerful in that respect. What, what are the main technical elements of this experiments?What are the possible problems? Well, the biggest problem besides getting the rig set up, getting the rig set up is not trivial. A lot of people have the rig, but just making sure it works well for your application.The financial element is not, you know, is not small. So there's the setting up of the apparatus and then, and then once you gather the data, there's a real data analysis issue. It takes these movie, if, so, if you're gathering 40 movies each with 30 cells, so you have 40 in 40 fields that you just filmed and each of those fields has 30 cells, then you have to sit down with each one of those movies and look at each one of those cells in that movie.So it takes a really long time to do data analysis. And that's, so it takes a week to analyze data that it took you two days to collect. So there's a, that's a very big obstacle in terms of gathering lots of data.So it's not, while it's high throughput data collection, it's not high throughput analysis. So what would you make to make this experiment really high throughput? So our lab is in the process of developing a program that does the data analysis automatically.And it's a pretty decent program. It's not perfect. The human eye is always better, but it's getting there.So What are the interesting application of this experiment? Maybe other fields you can mention? Besides cell division?You mean for the, oh, for the live cell imaging? Yes.Well, you can do cell motility assays. You can, depending on the, the microscopy that's involved, you can, you know, look at how cells, you could look at tumor development if you really wanted to.But there, you, you're, if as long as you have the right imaging set up, you can, you're pretty much not limited in terms of anything. Our scope happens to be just a wide field and we use 20 by 20 x objective. So it's a pretty low resolution set up, but that's sufficient for what we need to see.So there are, but there definitely aren't, you wouldn't be limited. Anything that has any dynamic kind of experiment, which is biology, it would be, would really benefit from lifestyle imaging. So calcium imaging, although you couldn't do it on our apparatus, that's a live cell.You definitely need live cell imaging for that. Like I said, patient assays, motility assays, those kinds of things would really benefit from live cell imaging. Would you like to tell a few words about your previous experience?How long do you work on these techniques? What do you you work on before? Wow.So in my PhD I didn't work.I did a lot of confocal microscopy, but no lifestyle imaging. And so during my postdoc I was, I helped Dr.King get this apparatus that, that we just used to set up these fragment, get that up and running. And, and so during my postdoc, I had a lot of experience with both widefield with TimeLapse imaging on our scope, which is the widefield and also TimeLapse imaging with confocal microscopy.And I would say that it's really powerful. You get a lot of information, but it's not easy to get working perfectly. So it really requires a lot of attention and a lot of attention to details at every little step of the process.And, and then you still have problems. So it's a tough application, but you can really get, if, if it's what you need to have to, to study your problem, your scientific problem, then it, it's invaluable in that respect, but it's tough. So find someone who knows what they're doing and use them.That's my advice. So good.

Długoterminowe Imaging komórkach ssaków przy dużym polu Mikroskopia

Intranuclear Microinjection of DNA into Dissociated Adult Mammalian Neurons

Primary neuronal cell cultures are valuable tools to study protein function since they represent a more biologically relevant system compared to immortalized cell lines. However, the post-mitotic nature of primary neurons prevents effective heterologous protein expression using common procedures such as electroporation or chemically-mediated transfection. Thus, other techniques must be employed in order to effectively express proteins in these non-dividing cells.
In this article, we describe the steps required to perform intranuclear injections of cDNA constructs into dissociated adult sympathetic neurons. This technique, which has been applied to different types of neurons, can successfully induce heterologous protein expression. The equipment essential for the microinjection procedure includes an inverted microscope to visualize cells, a glass injection pipet filled with cDNA solution that is connected to a N2(g) pressure delivery system, and a micromanipulator. The micromanipulator coordinates the injection motion of microinjection pipet with a brief pulse of pressurized N2 to eject cDNA solution from the pipet tip. 
This technique does not have the toxicity associated with many other transfection methods and enables multiple DNA constructs to be expressed at a consistent ratio. The low number of injected cells makes the microinjection procedure well suited for single cell studies such as electrophysiological recordings and optical imaging, but may not be ideal for biochemical assays that require a larger number of cells and higher transfection efficiencies. Although intranuclear microinjections require an investment of equipment and time, the ability to achieve high levels of heterologous protein expression in a physiologically relevant environment makes this technique a very useful tool to investigate protein function.

Direct intranuclear injection of cDNA is an effective transfection technique for post-mitotic cells. This method provides high levels of heterologous protein expression from single or multiple cDNA constructs and enables protein function to be studied in a physiologically relevant environment with a variety of single cell assays.

Primary neuronal cell cultures are valuable tools to study protein function since they represent a more biologically relevant system compared to ...

Dissection of Organs from the Adult Zebrafish

Over the last 20 years, the zebrafish has become a powerful model organism for understanding vertebrate development and disease.  Although experimental analysis of the embryo and larva is extensive and the morphology has been well documented, descriptions of adult zebrafish anatomy and studies of development of the adult structures and organs, together with techniques for working with adults are lacking. The organs of the larva undergo significant changes in their overall structure, morphology, and anatomical location during the larval to adult transition.  Externally, the transparent larva develops its characteristic adult striped pigment pattern and paired pelvic fins, while internally, the organs undergo massive growth and remodeling.  In addition, the bipotential gonad primordium develops into either testis or ovary.  This protocol identifies many of the organs of the adult and demonstrates methods for dissection of the brain, gonads, gastrointestinal system, heart, and kidney of the adult zebrafish. The dissected organs can be used for in situ hybridization, immunohistochemistry, histology, RNA extraction, protein analysis, and other molecular techniques.  This protocol will assist in the broadening of studies in the zebrafish to include the remodeling of larval organs, the morphogenesis of organs specific to the adult and other investigations of the adult organ systems.

This protocol describes a procedure for identifying and dissecting organs from the adult zebrafish.

Over the last 20 years, the zebrafish has become a powerful model organism for understanding vertebrate development and disease.  Although experimental ...

MISSION esiRNA for RNAi Screening in Mammalian Cells

RNA interference (RNAi) is a basic cellular mechanism for the control of gene expression. RNAi is induced by short double-stranded RNAs also known as small interfering RNAs (siRNAs). The short double-stranded RNAs originate from longer double stranded precursors by the activity of Dicer, a protein of the RNase III family of endonucleases. The resulting fragments are components of the RNA-induced silencing complex (RISC), directing it to the cognate target mRNA. RISC cleaves the target mRNA thereby reducing the expression of the encoded protein1,2,3. RNAi has become a powerful and widely used experimental method for loss of gene function studies in mammalian cells utilizing small interfering RNAs.
Currently two main methods are available for the production of small interfering RNAs. One method involves chemical synthesis, whereas an alternative method employs endonucleolytic cleavage of target specific long double-stranded RNAs by RNase III in vitro. Thereby, a diverse pool of siRNA-like oligonucleotides is produced which is also known as endoribonuclease-prepared siRNA or esiRNA. A comparison of efficacy of chemically derived siRNAs and esiRNAs shows that both triggers are potent in target-gene silencing. Differences can, however, be seen when comparing specificity. Many single chemically synthesized siRNAs produce prominent off-target effects, whereas the complex mixture inherent in esiRNAs leads to a more specific knockdown10.
In this study, we present the design of genome-scale MISSION esiRNA libraries and its utilization for RNAi screening exemplified by a DNA-content screen for the identification of genes involved in cell cycle progression. We show how to optimize the transfection protocol and the assay for screening in high throughput. We also demonstrate how large data-sets can be evaluated statistically and present methods to validate primary hits. Finally, we give potential starting points for further functional characterizations of validated hits.

Here we use a human esiRNA library in a high-throughput screen for genes involved in cell division. We demonstrate how to set up and conduct an esiRNA screens, as well as how to analyze and validate the results.

RNA interference (RNAi) is a basic cellular mechanism for the control of gene expression. RNAi is induced by short double-stranded RNAs also known as ...

Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation between cells within a tissue. Isolation of pure cardiomyocyte populations through a collagenase perfusion of mouse hearts facilitates the generation of single cell microarrays for whole transcriptome gene expression, or qPCR of specific targets using nanofluidic arrays. We describe here a procedure to examine single cell gene expression profiles of cardiomyocytes isolated from the heart. This paradigm allows for the evaluation of metrics of interest which are not reliant on the mean (for example variance between cells within a tissue) which is not possible when using conventional whole tissue workflows for the evaluation of gene expression (Figure 1). We have achieved robust amplification of the single cell transcriptome yielding micrograms of double stranded cDNA that facilitates the use of microarrays on individual cells. In the procedure we describe the use of NimbleGen arrays which were selected for their ease of use and ability to customize their design. Alternatively, a reverse transcriptase - specific target amplification (RT-STA) reaction, allows for qPCR of hundreds of targets by nanofluidic PCR. Using either of these approaches, it is possible to examine the variability of expression between cells, as well as examining expression profiles of rare cell types from within a tissue. Overall, the single cell gene expression approach allows for the generation of data that can potentially identify idiosyncratic expression profiles that are typically averaged out when examining expression of millions of cells from typical homogenates generated from whole tissues.

Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation ...

Analysis of Cell Cycle Position in Mammalian Cells

The regulation of cell proliferation is central to tissue morphogenesis during the development of multicellular organisms. Furthermore, loss of control of cell proliferation underlies the pathology of diseases like cancer. As such there is great need to be able to investigate cell proliferation and quantitate the proportion of cells in each phase of the cell cycle. It is also of vital importance to indistinguishably identify cells that are replicating their DNA within a larger population. Since a cell&prime;s decision to proliferate is made in the G1 phase immediately before initiating DNA synthesis and progressing through the rest of the cell cycle, detection of DNA synthesis at this stage allows for an unambiguous determination of the status of growth regulation in cell culture experiments.
DNA content in cells can be readily quantitated by flow cytometry of cells stained with propidium iodide, a fluorescent DNA intercalating dye. Similarly, active DNA synthesis can be quantitated by culturing cells in the presence of radioactive thymidine, harvesting the cells, and measuring the incorporation of radioactivity into an acid insoluble fraction. We have considerable expertise with cell cycle analysis and recommend a different approach. We Investigate cell proliferation using bromodeoxyuridine/fluorodeoxyuridine (abbreviated simply as BrdU) staining that detects the incorporation of these thymine analogs into recently synthesized DNA. Labeling and staining cells with BrdU, combined with total DNA staining by propidium iodide and analysis by flow cytometry1 offers the most accurate measure of cells in the various stages of the cell cycle. It is our preferred method because it combines the detection of active DNA synthesis, through antibody based staining of BrdU, with total DNA content from propidium iodide. This allows for the clear separation of cells in G1 from early S phase, or late S phase from G2/M. Furthermore, this approach can be utilized to investigate the effects of many different cell stimuli and pharmacologic agents on the regulation of progression through these different cell cycle phases.
In this report we describe methods for labeling and staining cultured cells, as well as their analysis by flow cytometry. We also include experimental examples of how this method can be used to measure the effects of growth inhibiting signals from cytokines such as TGF-&beta;1, and proliferative inhibitors such as the cyclin dependent kinase inhibitor, p27KIP1. We also include an alternate protocol that allows for the analysis of cell cycle position in a sub-population of cells within a larger culture5. In this case, we demonstrate how to detect a cell cycle arrest in cells transfected with the retinoblastoma gene even when greatly outnumbered by untransfected cells in the same culture. These examples illustrate the many ways that DNA staining and flow cytometry can be utilized and adapted to investigate fundamental questions of mammalian cell cycle control.

Determining the cell cycle position of a population of cells, or understanding how signals affect proliferation, can be readily measured by flow cytometry using this protocol. We report a simple experimental approach to staining cells and quantifying their position in the cell cycle.

The regulation of cell proliferation is central to tissue morphogenesis during the development of multicellular organisms. Furthermore, loss of control of ...

Measurement of Heme Synthesis Levels in Mammalian Cells

Heme serves as the prosthetic group for a wide variety of proteins known as hemoproteins, such as hemoglobin, myoglobin and cytochromes. It is involved in various molecular and cellular processes such as gene transcription, translation, cell differentiation and cell proliferation. The biosynthesis levels of heme vary across different tissues and cell types and is altered in diseased conditions such as anemia, neuropathy and cancer. This technique uses [4-14C] 5-aminolevulinic acid ([14C] 5-ALA), one of the early precursors in the heme biosynthesis pathway to measure the levels of heme synthesis in mammalian cells. This assay involves incubation of cells with [14C] 5-ALA followed by extraction of heme and measurement of the radioactivity incorporated into heme. This procedure is accurate and quick. This method measures the relative levels of heme biosynthesis rather than the total heme content. To demonstrate the use of this technique the levels of heme biosynthesis were measured in several mammalian cell lines.

Altered intracellular heme levels are associated with common diseases such as cancer. Thus, there is a need to measure heme biosynthesis levels in diverse cells. The goal of this protocol is to provide a fast and sensitive method to measure and compare the levels of heme synthesis in different cells.

Heme serves as the prosthetic group for a wide variety of proteins known as hemoproteins, such as hemoglobin, myoglobin and cytochromes. It is involved in ...

Efficient Mammalian Cell Expression and Single-step Purification of Extracellular Glycoproteins for Crystallization

Production of secreted mammalian proteins for structural and biophysical studies can be challenging, time intensive, and costly. Here described is a time and cost efficient protocol for secreted protein expression in mammalian cells and one step purification using nickel affinity chromatography. The system is based on large scale transient transfection of mammalian cells in suspension, which greatly decreases the time to produce protein, as it eliminates steps, such as developing expression viruses or generating stable expressing cell lines. This protocol utilizes cheap transfection agents, which can be easily made by simple chemical modification, or moderately priced transfection agents, which increase yield through increased transfection efficiency and decreased cytotoxicity. Careful monitoring and maintaining of media glucose levels increases protein yield. Controlling the maturation of native glycans at the expression step increases the final yield of properly folded and functional mammalian proteins, which are ideal properties to pursue X-ray crystallography. In some cases, single step purification produces protein of sufficient purity for crystallization, which is demonstrated here as an example case.

This is a quick, cost-efficient protocol for the production of secreted, glycosylated mammalian proteins and subsequent single-step purification with sufficient yields of homogenous protein for X-ray crystallography and other biophysical studies.

Production of secreted mammalian proteins for structural and biophysical studies can be challenging, time intensive, and costly. Here described is a time ...

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Knowledge about the localization of proteins in cellular subcompartments is crucial to understand their specific function. Here, we present a super-resolution technique that allows for the determination of the microcompartments that are accessible for proteins by generating localization and tracking maps of these proteins. Moreover, by multi-color localization microscopy, the localization and tracking profiles of proteins in different subcompartments are obtained simultaneously. The technique is specific for live cells and is based on the repetitive imaging of single mobile membrane proteins. Proteins of interest are genetically fused with specific, so-called self-labeling tags. These tags are enzymes that react with a substrate in a covalent manner. Conjugated to these substrates are fluorescent dyes. Reaction of the enzyme-tagged proteins with the fluorescence labeled substrates results in labeled proteins. Here, Tetramethylrhodamine (TMR) and Silicon Rhodamine (SiR) are used as fluorescent dyes attached to the substrates of the enzymes. By using substrate concentrations in the pM to nM range, sub-stoichiometric labeling is achieved that results in distinct signals. These signals are localized with ~15&#8211;27 nm precision. The technique allows for multi-color imaging of single molecules, whereby the number of colors is limited by the available membrane-permeable dyes and the repertoire of self-labeling enzymes. We show the feasibility of the technique by determining the localization of the quality control enzyme (Pten)-induced kinase 1 (PINK1) in different mitochondrial compartments during its processing in relation to other membrane proteins. The test for true physical interactions between differently labeled single proteins by single molecule FRET or co-tracking is restricted, though, because the low labeling degrees decrease the probability for having two adjacent proteins labeled at the same time. While the technique is strong for imaging proteins in membrane compartments, in most cases it is not appropriate to determine the localization of highly mobile soluble proteins.

Here, we present a protocol for multi-color localization of single membrane proteins in organelles of live cells. To attach fluorophores, self-labeling proteins are used. Proteins, located in different membranes compartments of the same organelle, can be localized with a precision of ~18 nm.

Knowledge about the localization of proteins in cellular subcompartments is crucial to understand their specific function. Here, we present a ...

Anaerobic Growth and Maintenance of Mammalian Cell Lines

Most mucosal surfaces along with the midpoints in tumors and stem cell niches are geographic areas of the body that are anoxic. Previous studies show that the incubation in normoxic (5% CO2 in air) or hypoxic (low oxygen levels) conditions followed by an anoxic incubation (an absence of free oxygen) results in limited viability (2&#8211;3 days). A novel methodology was developed that enables an anoxic cell cultivation (for at least 17 days; the maximum time tested). The most critical aspect of this methodology is to ensure that no oxygen is introduced into the system. This is obtained by the degassing of media, and by flushing tubes, dishes, flasks, and pipettes with an anaerobic gas mixture (H2, CO2, N2) followed by permitting the materials to equilibrate to the anoxic (non-oxygen) environment prior to usage.&#160;Additional care must be exercised when acquiring photomicrographs to ensure that the micrographs obtained do not contain artifacts. In the absence of oxygen, cell morphology is significantly altered. Two distinct morphotypes are noted for all anaerobically-grown cells. The ability to grow and maintain mammalian cells in the absence of oxygen can be applied to the analysis of cell physiology, polymicrobial interactions, and the characterization of biosynthetic pathways for novel cancer drug development.

Here, we describe a new method that enables the anaerobic long-term cultivation of established cell lines. The maximum survival time that was tested is 17 days. This method is suitable for the testing of cytotoxic agents and exploring the physiology of anoxically replicating cells.

Most mucosal surfaces along with the midpoints in tumors and stem cell niches are geographic areas of the body that are anoxic. Previous studies show that ...