How do I homogenise a small pellet of 10 to 50 cells within less than 50 µL?

How do I homogenise a small pellet of 10 to 50 cells within less than 50 µL?

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I have a problem with declumping/ homogenising bacterial cells in small volumes (10 to 50 µL). I know that I can get all of my cells out of the tube, but they are still stuck together and won't form separate colonies on an agar dish.
I know that because when I take the last 50 µl from a 1 ml tube and put it in a new tube and mix the 50 µL with a larger volume (e.g. 100 µL), we get a higher CFU count.

Since the discovery of the composition and structure of the mammalian cell membrane, biologists have had a clearer understanding of how substances enter and exit the cell’s interior. The selectively permeable nature of the cell membrane allows the movement of some solutes and prevents the movement of others. This has important consequences for cell volume and the integrity of the cell and, as a result, is of utmost clinical importance, for example in the administration of isotonic intravenous infusions. The concepts of osmolarity and tonicity are often confused by students as impermeant isosmotic solutes such as NaCl are also isotonic however, isosmotic solutes such as urea are actually hypotonic due to the permeant nature of the membrane. By placing red blood cells in solutions of differing osmolarities and tonicities, this experiment demonstrates the effects of osmosis and the resultant changes in cell volume. Using hemoglobin standard solutions, where known concentrations of hemoglobin are produced, the proportion of hemolysis and the effect of this on resultant hematocrit can be estimated. No change in cell volume occurs in isotonic NaCl, and, by placing blood cells in hypotonic NaCl, incomplete hemolysis occurs. By changing the bathing solution to either distilled water or isosmotic urea, complete hemolysis occurs due to their hypotonic effects. With the use of animal blood in this practical, students gain useful experience in handling tissue fluids and calculating dilutions and can appreciate the science behind clinical scenarios.

the movement of water and small molecules across the selectively permeable membranes of mammalian cells is a fundamental concept of physiology. These processes can be difficult for students to visualize and appreciate, and it is often left to images in textbooks or online animations to explain such movements. This practical uses animal blood bathed in solutions with differing osmolarities and tonicities to explore the concept of water movement by osmosis and the resultant hemolysis that can occur when red blood cells are exposed to hypotonic solutions. Students are given the opportunity to handle body fluids, practice preparing dilutions, and make accurate observations.


The resulting diversity allows for histones to play key roles in almost all transcriptional processes, and the dysregulation of such processes has been associated with various diseases. In particular, transcriptional alterations have been noted in cancer, where they have been associated with its progression, aggressiveness, and metastasis, and are useful in its prognosis (Dawson & Kouzarides, 2012 ). A deep understanding of histone variants and proteoforms is thus needed to comprehend both normal cellular function and disease. A key issue, however, is that conventional PTM analysis methods do not capture proteoforms. A high-throughput middle-down or top-down proteomics approach is instead necessary to address these questions (Holt et al., 2019 Wang, Holt, & Young, 2018 ). In these proteomic approaches, either the N-terminal tail or the entire protein is analyzed intact and the relationships between PTMs are maintained, and thus, proteoform-level information is captured.

The sample preparation step has become a bottleneck for current strategies for the analysis of histone PTMs. Most prior protocols are not optimized for low sample amounts and are often quite inefficient with larger sample volumes. In addition, many have included substantially long incubation times with the expectation of improved efficiency with longer times however, we have found that shorter times are both expeditious and equally or even more efficient. Speed also limits degradation processes, such as oxidation.

The protocols described here are an optimization and extension of previous histone extraction protocols progressively improved over more than 130 years (Kossel, 1884 Shechter, Dormann, Allis, & Hake, 2007 ). We describe two protocols that have been streamlined and optimized for three different starting tissue culture cell amounts. In Basic Protocol 1, cells are lysed, nuclei are isolated, and histones are acid extracted. In Basic Protocol 2, histones are purified and separated by histone family with reversed-phase high-performance liquid chromatography (HPLC), and histone H4 is proteoform resolved by HPLC and analyzed with top-down mass spectrometry. Additionally, we briefly describe a protocol to obtain intact histone H3 tails for middle-down proteomics (Support Protocol).

These protocols can be used together with multiple other proteomics protocols for the analysis of histones (Chen et al., 2016 Dang et al., 2014 Dang et al., 2016 Gargano et al., 2018 Holt, Wang, & Young, 2019 Moradian, Kalli, Sweredoski, & Hess, 2014 Plazas-Mayorca et al., 2009 Sidoli & Garcia, 2017 Sidoli, Bhanu, Karch, Wang, & Garcia, 2016 Wang et al., 2018 Young et al., 2009 Zhou et al., 2019 ). They also are useful for a variety of non-proteomics applications. Our goals in developing these protocols were 1) to increase throughput, such that the entirety of the pipeline could be performed on a single day 2) to decrease sample requirements, such that small tissue samples can be successfully analyzed and 3) to not compromise data quality and reproducibility. The expediency of these protocols makes them an excellent choice for other chromatin biology approaches that are limited by sample size or throughput. Even in the absence of limiting sample and throughput constraints, the use of these protocols will substantially expedite and simplify histone extraction without any observed cost in quality.


1) Cell labeling

  1. Prepare cell suspensions.
    • This protocol has been optimized using fresh human PBMCs isolated using Ficoll gradients and mouse splenocytes prepared using mechanical dissociation. If using cells isolated from whole lysed blood or other sample types, users may need to optimize staining concentrations.
    • BioLegend has not tested this protocol using single cell suspensions derived from enzymatically digested tissue. Enzymatic digestion may result in cleavage of epitopes and result in reduced staining with TotalSeq&trade antibodies. Optimization of staining conditions and concentrations may be required.
  2. Assess Cell Viability.
    • Carefully count all cells to ensure accurate quantitation and assess cell viability.
    • Ideal cell viability is &ge 95%.
      • Low cell viability is associated with generation of poor data and is not ideal for single cell experimentation.
      • If low cell viability is observed, users may need to enrich live cells or repeat cell suspension preparation.
    • Contact Technical Services with any questions regarding cell viability. BioLegend uses Countess II for counting and assessing cell viability using the following protocol, however other methods for assessing cell viability are suitable. For more information about the protocol used by BioLegend, see the following link, details can be found under &ldquoPBMC viability assessment&mdashgeneral methods&rdquo.
  3. Dilute cells in appropriate volume prior to staining.
    • If working with human cells, dilute 1 million cells in 45&muL of Cell Staining Buffer in 12 x 75mm flow cytometry tubes.
    • If working with mouse cells, dilute 1 million cells in 49.5&muL of Cell Staining Buffer in 12 x 75mm flow cytometry tubes.
  4. Block cells.
    • Add 5 µl of Human TruStain FcX&trade Fc Blocking reagent or 0.5 µl of TruStain FcX&trade PLUS (anti-mouse CD16/32) antibody. The final blocking volume should be 50 µl.

Note: We no longer recommend the use of dextran or monocyte blocker during blocking/staining. If you have any questions please contact BioLegend Technical Support.

  • If using biotinylated antibodies, we recommend staining with your primary antibody first followed by staining with streptavidin TotalSeq&trade conjugates. Do not stain with more than 1 unique biotinylated antibody to use for detection.
  • If using an antibody cocktail larger than 50 µL, contact Tech Services or local Technical Applications Scientists for protocol guidance before proceeding with this protocol.
  • Transfer Curiox Wash Plate to the Curiox Laminar Wash System and wash using the following parameters (Flow Rate: 10, # of cycles: 25)
  • Remove Curiox Wash Plate from the system and add 40&muL of wash buffer to the well containing the washed cells.
  • Resuspend by pipetting gently.
  • Proceed to step 11.
  • BioLegend recommends the use of a swing bucket centrifuge as centrifuging with fixed angle rotors may lead to &ldquosmearing&rdquo of the cell pellet, which may result in cell loss. Contact Tech or local field representative if you have any questions.
  • BioLegend recommends manually pouring out supernatant being careful not to disrupt the cell pellet. Between 50-150 &muL of residual supernatant will remain in the tube after decanting, which is taken into consideration in step 11 of this protocol. Do not try to forcefully remove any remaining liquid as this will disrupt the cell pellet and result in cell loss.

Note: 40 µm Flowmi&trade Cell Strainer may be too small for some sample types.

Note: If using 10x Genomics Chromium Controller for single cell partitioning, we highly recommend determining cell viability. Ideally the viability should be >90% after filtration for optimal capture rate. The presence of a large number of non-viable cells can decrease the efficiency of cell partitioning and recovery within the 10x Genomics Chromium chip.

II) Run 10x Genomics single cell 3' v3 or v3.1 assay as described through Post Gem-RT Cleanup &ndash Dynabeads (step 2.1). 10x Genomics Documents CG000185_Rev_D for v3 assay or CG000206_Rev_D for v3.1.

At cDNA amplification step (Step 2.2), use the following table:

ADT 1 rxn (µl) HTO 1rxn (µl) ADT + HTO 1 rxn (µl)
Amp Mix 50 50 50
cDNA Primers* 15 15 15
ADT Additive Primer (0.2 µM stock) 1 0 1
HTO Additive primer v2 (0.2 uM stock) 0 1 1
Total 66 66 67

* included with 10x Genomics 3&rsquo kit, different from Feature cDNA primers 2.

Follow steps 2.3A and 2.3B exactly to separate ADTs/HTOs from cDNA. Continue to use 70 µL of sparQ or SPRI beads in step 2.3B.

See notes at the end of the protocol for further details on primer sequences.

III) ADT and mRNA library preparation

Note: For samples that contain both ADT and HTO, perform two separate reactions and add 5 µl of &ldquopurified ADT/HTO fraction&rdquo from the same sample to ADT or HTO reaction.

  • C) Post ADT/HTO library amplification clean-up
  1. Add 120 µl sparQ or SPRIselect Reagent (1.2X) to each sample.
  2. Incubate 5 min at room temperature.
  3. Place on the magnet in its High position until the solution clears.
  4. Carefully remove and discard the supernatant.
  5. Place tubes on magnet in its High position. Wash pellet twice with 200 µl 80% ethanol.
  6. Centrifuge briefly. Place on the magnet Low. Remove remaining ethanol.
  7. Remove from the magnet. Add 40.5 µl water.
  8. Incubate 2 min at room temperature.
  9. Place on the magnet in its Low position until the solution clears.
  10. Transfer 40 µl to a new tube strip. Store at 4°C for up to 72 h or at &minus20°C for long-term storage.
  11. ADT/HTO libraries are now ready to be sequenced
    Quantify libraries by standard methods (QuBit, BioAnalyzer, qPCR).
    ADT libraries will be around 180 bp (Figure 1B).

Sequencing CITE-seq libraries:

To obtain sufficient read coverage for both libraries, we typically sequence ADT libraries in 5-10% of a lane and cDNA library fraction at 90% of a lane (HiSeq2500 Rapid Run Mode Flow Cell). See table below for sequencing depth recommendations

Minimum Sequencing Depth

3' Gene Expression Library

Cell Surface Protein Library <100 ADT panel

Cell Surface Protein Library &ge100 ADT panel

TotalSeq&trade antibodies. Each clone is barcoded with a unique oligonucleotide sequence. These contain standard small TruSeq RNA read 2 sequences and can be amplified using Illumina&rsquos Truseq Small RNA primer sets (RPIx &ndash primers, see example RPI1 below)


Please visit for detailed information:

Oligos required for ADT library amplification:

  • PAGE purification is the preferred method when ordering primers.
  • The phosphorothioate bonds in the primer renders the oligonucleotide resistant to nuclease degradation.
  • A unique Illumina primer (index) should be used for each 10x Genomics sample lane used within one sequencing lane.
  2. ADT cDNA PCR additive primer 5&rsquoCCTTGGCACCCGAGAATT*C*C
  4. Illumina Small RNA RPI1 primer (for ADT amplification i7 index 1, Oligonucleotide sequences, Illumina) 5&rsquoCAAGCAGAAGACGGCATACGAGAT CGTGAT GTGACTGGAGTTCCTTGGCACCCGAGAATTC*C*A

* indicates a phosphorothioate bond
B indicates C or G or T not A nucleotide

Primers Used for Sequencing Library Construction:

Do Not Use with 10x Index






















Background Information

Critical Parameters

There are specific safety concerns to be addressed when preparing immunogold-labeled grids and disposing of them. Certain samples, including some live viruses, will require special handling according to their BSLs due to their infectious nature. For example, pandemic H1N1 influenza virus requires BSL-2 handling. Additionally, some negative staining solutions require proper disposal. For example, UA is toxic and contains trace levels of radioactivity, and thus it must be disposed of in radioactive waste. These concerns should be addressed when designing experiments.

Many variables must be properly optimized to successfully prepare immunogold-labeled grids for TEM (see Troubleshooting). It is also important to note that the order in which variables are optimized can greatly affect productivity. It is highly recommended to first optimize negative staining of unlabeled samples, since interpretation of labeling requires good contrast from negative stain. This technique will also reveal basic morphology of the sample for comparison with immunogold-labeled samples. Second, the antibody concentrations should be optimized to ensure successful labeling of the antigen of interest while maintaining low background labeling. Serial antibody dilutions may be useful to identify an appropriate concentration.

Controls are essential to interpreting results from an immunoelectron microscopy experiment to account for nonspecific labeling and to account for morphology changes that may arise due to the presence of antibodies. Preparing the necessary controls requires additional grids to be made with different antibody combinations, which should be performed at the same time as the experimental grids. A control grid should be prepared with a primary antibody to an irrelevant antigen raised in the same host species as the experimental antibody, followed by the same secondary antibody as used for the experimental grid. This control will account for nonspecific labeling to ensure specific binding to the target antigen. If available, using an alternative on-target antibody from a different species could be an excellent method to validate immunoelectron microscopy results. To control for changes in the appearance of a complex when labeled with antibody, another control grid should be prepared with the experimental primary antibody combined with a secondary antibody that is not conjugated with colloidal gold. This control will indicate how morphology may change in the presence of antibodies. For example, small particulates may appear larger in the presence of antibodies, as the antibodies themselves may appear by negative stain. This can be seen for the Flublok (Sanofi Pasteur) viral vaccine candidate in Figure 2, in which the immunogold-labeled complexes appear larger than nonlabeled complexes. If performing immunoelectron microscopy targeting multiple antigens simultaneously (Alternate Protocol), cross-reactivity between antibodies should be tested. For example, when an experiment is designed to label two antigens, one with a mouse antibody and one with a rabbit antibody, two additional control grids are needed. In the first, the mouse primary antibody should be used followed by a gold-conjugated secondary antibody targeting rabbit. The opposite should be prepared for the second grid.

TEM imaging parameters are critical to successfully visualize immunogold-labeled samples and interpret data. It is important to ensure the microscope is well aligned, grids are imaged slightly under focus, and an appropriate magnification is chosen such that multiple particulates can be visualized in one field of view while still detecting details of the particles. Specific operating instructions for TEM are dependent on the microscope used and are beyond the scope of this section.


Immunoelectron microscopy has multiple steps at which problems can occur. Unfortunately, for any given grid, the errors often do not become obvious until the very end of the procedure, when grids are imaged by TEM. The most common problems that occur are poor sample quality, lack of labeling or nonspecific labeling, and poor visualization of sample.

Sample quality should be assessed through negative staining of the sample alone prior to beginning immunogold-labeling experiments. Samples must be properly optimized for concentration and distribution on the grid. The concentration of viral particulate, whether virus, purified viral protein, or viral vaccine, should be low enough to resolve individual particles but high enough that multiple particles can be viewed in a single field of view at the preferred magnification of the microscope. Additionally, particles should be well dispersed to successfully identify with which particle the colloidal gold labels are associated. The ideal sample should be relatively free of contamination and thus may require purification steps. When preparing grids for imaging, if a sample does not adhere well to the grid, the grid can be pretreated with a wetting agent, such as poly- L -lysine, or can be made more hydrophilic using a glow discharge unit prior to application of the sample. If structural details of the sample cannot be resolved in TEM prior to immunogold labeling, the negative stain grain size may be too large, and a stain with higher resolution may be required. For example, UA has a smaller grain size and therefore higher resolution than PTA. Still, the uncertainty in the location of an epitope relative to the gold marker far exceeds the granular resolution of all commonly used negative staining solutions. Differences between common negative staining solutions are discussed in Support Protocol 2. Upon immunogold labeling, if multiple particles are proximal to a gold marker and it is unclear which particle has been labeled, sample concentration may be too high. In this case the sample should be diluted, preferably with a low-salt buffer. The appearance of the sample may be altered by immunogold staining due to the multiple wash steps in the presence of mild detergent (e.g., if the sample has a membrane that may become deformed). In this case the sample can be fixed with a cross-linking agent, such as paraformaldehyde or glutaraldehyde, prior to incubation with antibody.

Poor labeling may be caused by multiple factors. One cause could be if there is a very low concentration of antigen. It could be also be caused by an antibody that may have lost its binding ability, or there may be incompatibility between antibody and blocking solution or subsequent negative staining solution. Antibody binding to the sample should be verified in this case by another means, such as through an enzyme-linked immunosorbent assay (ELISA). Antibody concentration could also be increased. If gold labeling saturates at a level much lower than expected even with sufficiently high antibody concentrations (Fig. 4A), it could be that antigens are very close together, and there may be steric hindrance between neighboring antibodies and/or their gold labels that prevents additional gold-conjugated antibodies from binding. In this case, it may be necessary to use a smaller size of colloidal gold–conjugated antibody (e.g., 10-nm gold-labeled antibody instead of 25-nm gold-labeled antibody). If labels are not only binding the antigen of interest but also nonspecifically present in the background (Fig. 4B), wash steps could be inadequate, in which case additional wash steps can be added. Alternatively, antibody concentration could be too high, and primary and/or secondary antibodies may need further dilution. If the gold labels appear to be clustered irrelevant of antigen presentation (Fig. 4B, arrows), this could be due to natural amplification of secondary antibodies bound to primary antibodies, in which case further dilution of secondary antibody can be performed, or to clumped primary antibody, in which case a fresh sample of primary antibody may be needed. An alternate solution to optimizing two sets of antibodies would be to use a colloidal gold–conjugated primary antibody, which then precludes the need to use any secondary antibody solutions. Doing so eliminates concerns of nonspecific binding by the secondary antibody, although nonspecific binding of the primary antibody may still be an issue. However, few colloidal gold–conjugated primary antibodies exist on the market, so it may be necessary to chemically conjugate colloidal gold to an antibody of interest, which is beyond the scope of this article.

Poor visualization of the sample may be caused by too light or too heavy negative staining, as a result of positive staining (Ackermann, 2013 ), or due to broken carbon on the grid itself. General negative staining procedures should be addressed prior to beginning immunogold labeling through negative staining of sample alone. Successfully negative-stained particles should appear light in color, with the stain of the carbon support appearing as dark. If colors have inverted, where the particles appear as dark on a light background, positive staining has occurred, and negative staining conditions may need to be reevaluated. Possible solutions include altering the negative staining solution used or modifying the concentration of the stain to alter the amount of stain present on the grid. If staining is too weak (Fig. 4C), it will be difficult to identify and analyze particulates on the grid in this case, the concentration of negative stain can be increased. If staining is too dense, this could lead to difficulties in visualizing the sample on the grid as the sample may appear opaque or have poor contrast compared to the background (Fig. 4D). To remedy this, a wash step can be performed after application of negative staining solution, or the concentration of negative stain can be lowered.

Heavy staining could also lead to broken carbon on the grid. Grids with broken carbon would appear empty under the microscope, as if no stain or sample is present (Fig. 4E, left). Another cause of broken carbon could be due to using old grids, as carbon becomes more fragile over time. The carbon could also have become brittle if treated in a glow discharge unit multiple times or for a very long incubation. In these cases, new grids may need to be acquired. Rough handling of the grid may lead to breaking of the carbon. In this case, using grids that have formvar plastic support along with carbon may be beneficial. If the grid appears to have patches of irregular particles under the microscope, it could be because the grid dried during preparation. It is critical to keep the grid hydrated throughout the entire immunogold staining procedure. It may be necessary to increase droplet size of liquids on Parafilm or perform all steps in a humidified chamber (Fig. 1). Patchy grids could also be caused by interactions between buffer and the negative staining solution (Fig. 4F). PTA interacts with phosphate buffers to produce fractal-like artifacts, while UA interacts with phosphate buffers to create crystal-like artifacts. In this case additional wash steps with distilled water may be necessary before applying the negative staining solution to the grids.

Understanding Results

Statistical analyses

Immunogold labeling of viral particulate suspensions is often interpreted through qualitative and visual means. Statistical analyses, such as calculating and analyzing the number of gold labels associated with a specific particulate, can be done but are dependent on the sample being studied and therefore beyond the scope of this article. When doing so, however, it is important to keep in mind that antibodies may not fully bind to the antigen of interest or that not every primary antibody will be bound by a colloidal gold–conjugated secondary antibody, as these will depend on steric hindrance, antibody concentrations, and binding rates. Any quantitation that is done must also take into consideration the amount of background labeling that appears when colloidal gold labels are found on the support film with no viral particles present. The ratio of specifically to nonspecifically bound gold labels is important for estimating the error of an immunogold labeling experiment. To calculate the ratio, collect multiple TEM micrographs at the desired magnification, and determine an average number of nonspecific colloidal gold particles over all of the micrographs. Compare that to the number of antigen-bound colloidal golds. An acceptable ratio for binding specificity during immunogold labeling would be at least 10:1, otherwise erroneous interpretations of the labeling are likely.

Typical results

Antigens of interest can be identified using colloidal gold labels after immunoelectron microscopy methods. The figures provide examples of successful labeling of hemagglutinin glycoproteins, both on the commercially available Flublok (Sanofi Pasteur) influenza vaccine sample (Fig. 2) and live influenza A H1N1 virus (Fig. 3). Immunogold labels outline the edges of both samples, as glycoproteins protrude on the exterior of both samples with minimal background labeling. This is in stark contrast to negative staining microscopy of the same samples, which displays the morphology of virus and vaccine samples without colloidal gold labels. Note that the background of the immunogold-labeled sample appears to be fully coated with protein, compared with the pure carbon background of sample that is simply negative stained. This is because blocking with BSA leads to nonspecific albumin deposition on the grid, which can be visualized in the background but does not interfere with labeling.

Time Considerations

Immunogold staining requires several hours to complete due to many long incubations and repeated careful grid handling. It is therefore recommended to do several grids in parallel to maximize the time cost, as preparing additional grids does not significantly lengthen the time needed. The authors recommend always performing negative staining TEM on any samples before immunogold staining protocols are begun, as negative staining can be done relatively quickly and can be done to ensure the sample conditions are optimized for immunogold staining.


This work was supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health, and The Biomedical Advanced Research and Development Authority.


Phenotypic Characterization of iMSC and adMSC

Both iMSC and adMSC were assessed for MSC characteristics and identification criteria, as established by the International Society for Cellular Therapy 46 . Flow cytometric analysis revealed high expression of MSC markers, including Sca-1, CD29, CD44, CD73, and CD106. In contrast, there was no expression of leukocyte markers, including CD45, CD11b, and CD31 (Fig. 1A). We found that whereas adMSC highly expressed CD90, the iMSC were negative for CD90 expression. Both cell types were positive for expression of CD106, which was expressed exclusively on MSC and not on skin fibroblasts (not shown). Tri-lineage differentiation studies also revealed that both adMSC and iMSC could be differentiated into adipocytes, osteoblasts, and chondrocytes (Fig. 1B).

Phenotypic characterization of iMSC and adMSC. (A): Expression of cell surface determinants using flow cytometry. Specific antibody staining depicted in red, whereas isotype control staining for each displayed in blue. The percentage of the positive staining for each marker was indicated. (B): Tri-lineage differentiation of passage 3 MSC, as described in “Materials and Methods” section. Adipocytes detected using Oil Red O (×200) chondrogenesis by Alcian Blue, and osteogenesis by Alizarin Red staining at day 12 after differentiation. Abbreviations: adMSC, adipose-derived mesenchymal stem cell iMSC, iPSC-derived mesenchymal stem cells.

Effects of MSC Administration on Clinical Signs and Colonic Lesions in Mice with DSS-Induced Colitis

Mice treated with DSS in their drinking water exhibited clinical signs consistent with colitis, including sustained weight loss, bloody diarrhea and abnormal fecal consistency resulting in decreased clinical scores over time (Fig. 2). Following the onset of signs of colitis, mice (n = 5 per treatment group) were treated with MSC (1 × 10 6 MSC per mouse per injection, i.v.) on days 10, 13, and 16. In mice treated with either iMSC or adMSC the clinical illness scores were significantly reduced compared to mice treated with DSS only (for iMSC-treated mice, p = .003 and for adMSC treated mice, p = .001 respectively) (Fig. 2A). The improvement in clinical scores became apparent within 1 day of the first MSC injection and was maintained during the period of MSC injections, whereas clinical scores continued to worsen in DSS-only treated animals.

Effects of mesenchymal stem cells (MSC) administration on clinical scores and inflammatory score in DSS-induced colitis mice. Mice were monitored for percentage of weight loss, fecal occult blood, and fecal consistency every day during DSS treatment period (19 days) and a total clinical score was calculated. (A): Clinical score over time in 4 groups of mice (n = 5 per group): control, DSS treated + PBS DSS + iMSC, and DSS + adMSC. MSC were administered by tail vein on days 10, 13, and 16. (B): Histology of colonic tissue sections from one mouse from each of 4 treatment groups, based on H&E stained sections. (C): Quantitative inflammatory scores assessed by H&E histopathology, as described in “Materials and Methods” section. Data was transformed to a normal distribution and the statistical differences were calculated using (A) repeated measures One-way ANOVA with Tukey's adjustment, (B) One-way ANOVA with Tukey's adjustment (*, p ≤ .05 **, p ≤ .01 ***, p ≤ .001 ****, p ≤ .0001). Scale bar indicated 50 µm. Abbreviations: adMSC, adipose-derived mesenchymal stem cell DSS, dextran sodium sulfate iMSC, iPSC-derived mesenchymal stem cells inj, injection ns, not significant PBS, phosphate-buffered saline.

Gross lesion scores and histopathology were evaluated in distal colon tissues collected in mice euthanized at 72 hours after the third MSC injection (day 19 of DSS treatment). In mice treated with DSS only (compared to untreated control animals), the colon appeared shortened and hyperemic (data not shown). Histologically, colonic tissues from DSS-treated mice exhibited severe infiltration of inflammatory cells, variable degrees of colonic gland ectasia and necrosis, extensive mucosal erosion to ulceration, and occasional complete loss and collapse of mucosal architecture (Fig. 2B) consistent with previous studies 42, 44, 47 . In contrast, colonic tissues from mice treated with either iMSC or adMSC exhibited an overall reduction in transmural inflammation, with significantly less infiltration of inflammatory cells in the lamina propria, diminished mucosal ulceration, and decreased mucosal collapse and granulation tissue formation. The overall histological inflammatory scores were significantly improved in mice treated with either iMSC or adMSC, compared to the PBS-treated group (iMSC, p = .002 adMSC, p = .003) (Fig. 2C). These results indicated therefore that iMSC were equivalent to adMSC in their ability to ameliorate the signs of intestinal inflammation induced by DSS treatment in this model of IBD.

Trafficking of MSC to Intestinal and Extraintestinal Tissues

To investigate the distribution of MSCs to sites of colonic inflammation, MSC were labeled using DiR dye immediately prior to injection. Cell distribution was monitored using IVIS live animal imaging. These studies revealed that labeled MSCs were primarily distributed to lungs initially, and at later time points labeled MSC could also be localized in the liver and spleen (Fig. 3A). However, labeled cells could not be detected by IVIS imaging in intestinal tissues.

Localization of labeled mesenchymal stem cells (MSC) in live animals and tissue sections. (A): For in vivo localization, MSCs were labeled using DiR dye before injection and mice were monitored by IVIS in vivo fluorescent imaging at 24 hours after the first injection, 72 hours post-injections and on the day of euthanasia. High concentrations of MSC were found in lung and liver and spleen, but not in intestinal tissues. (B): In vitro labeled MSC using fluorescent labeled (DiD). Localization of labeled MSCs in colonic mucosa and regional lymphoid tissues, tissues were collected 10 days after MSC administration. (C): Presence of labeled MSCs in spleen and mesenteric lymph node tissues. (D): Labeled MSC in colonic mucosa (arrow) presented in corresponding channel. (E): High power views of labeled MSC in colonic mucosa and submucosa. (F, G): Labeled MSC in Peyer's patch of colon (arrow). Scale bar indicated 50 µm in all panels except panel (B) (indicated 25 µm). Abbreviations: adMSC, adipose-derived mesenchymal stem cell Ctrl, control DSS, dextran sodium sulfate hrs, hours iMSC, iPSC-derived mesenchymal stem cells MLN, mesenteric lymph node PBS, phosphate-buffered saline.

To increase the sensitivity of cell detection, additional studies were done with mice injected with DiD-labeled MSC (Fig. 3B), followed by immunohistochemical examination of tissues from injected mice (Fig. 3C–3G). These studies revealed that labeled MSC could be detected only very rarely in the colonic mucosa and submucosa (Fig. 3D, 3E), in Peyer's patches (Fig. 3F, 3G) and mesenteric lymph nodes (Fig. 3C) of treated animals. In contrast, labeled MSC were much more numerous in the spleen (Fig. 3C) and lung tissues (data not shown). The relatively scarcity of MSC in colonic tissues following i.v. injection in DSS models is consistent with several previous studies 13, 22, 48, 49 , but differs markedly from findings in other published studies in which high numbers of labeled MSC were found in intestinal tissues 23, 50, 51 . Our findings, with very low numbers of MSC in colonic tissues, suggest that the therapeutic benefits of injected MSC were more likely to have been mediated by paracrine secreted factors than by direct cell-to-cell effects between MSC and colonic epithelial cells.

Effects of MSC Administration on Epithelial Regeneration

The histologic appearance of colonic tissue from mice treated with MSC demonstrated remarkable recovery of intestinal epithelial integrity (see Fig. 2B). These findings suggested therefore that MSC treatment may have affected directly the reconstitution of epithelial integrity in the colon. Stimulation of epithelial regeneration could be mediated by several factors, including enhanced neovascularization, increased intestinal stem cell regeneration, and greater crypt cell proliferation and differentiation 52, 53 . Therefore, immunohistochemistry was used to investigate these processes in colonic tissues of DSS-treated control mice and mice treated with iMSC and adMSC (Fig. 4).

Effects of mesenchymal stem cells (MSC) administration on intestinal epithelial cell proliferation, stem cell recruitment, and angiogenesis. (A): Immunoflourescence detection of Ki-67+ (red) cytokeratin+ (green) intestinal epithelial cells in colonic tissues from mice with DSS-induced colitis, with or without MSC treatment. (B): Graphical representation of numbers of Ki-67+ epithelial cells/5 villi. (C): Immunofluorescence detection of Lgr5+ intestinal stem cells (red) at the base of colon crypts in colonic tissues from control and MSC-treated mice. (D): Graphical representation of numbers of Lgr5+ stem cells per mm 2 tissue. (E): Immunofluorescence detection of CD31+ neoangiogenic cells in colonic tissues of control and MSC treated mice. (F): Graphical representation of the mean vessel density and mean vessel area/mm 2 of mucosa, as determined as described in “Materials and Methods” section. The statistics reported as mean ± SD, statistical differences were calculated using One-way ANOVA with Tukey's adjustment (*, p ≤ .05 **, p ≤ .01 ***, p ≤ .001 ****, p ≤ .0001). Scale bar indicated 50 µm in all panels. Abbreviations: adMSC, adipose-derived mesenchymal stem cell CKs, cytokeratin DAPI, 4′,6-diamidino-2-phenylindole DSS, dextran sodium sulfate iMSC, iPSC-derived mesenchymal stem cells Lgr5, leucine rich repeat containing G protein-coupled receptor 5 ns, not significant PBS, phosphate-buffered saline.

First, we observed a significant increase in the numbers of Ki-67+ intestinal epithelial cells in the colon of mice treated with iMSC and with adMSC, compared to the numbers of Ki-67+ epithelial cells in DSS-only treated animals and in untreated control animals (Fig. 4A, 4B). In addition, we found that the numbers of Lgr5+ intestinal stem cells were significantly greater in the colonic mucosa of animals treated with MSC, compared to DSS-only treated or control animals (Fig. 4C, 4D). Finally, MSC-treated animals demonstrated increased angiogenesis as determined by significantly increased numbers of CD31+ endothelial cells, as well as increased mean vessel density and area compared to control animals, (Fig. 4E, 4F).

Taken together, these findings indicate that systemic administration of iMSC or adMSC exerted an important trophic effect on intestinal epithelial cells, by stimulating proliferation and recruitment of intestinal stem cells, and on the overall intestinal blood supply, by stimulating local angiogenesis. Thus, these findings suggest secretion of multiple trophic factors by i.v. delivered MSC. The stem cell tracking data, and the paucity of MSC detected in colonic tissues, strongly suggests that these trophic factors were likely to have been produced at sites distant from the GI tract.

Administration of MSC Reverses Microbiome Dysbiosis

Previous studies have found that DSS-induced colitis causes alterations in the gut microbiome 54 . In our studies, we used 16S sequencing to investigate the composition of the gut microbiome of DSS-treated animals, and we also found marked alterations in the populations of several important gut phyla (Fig. 5). For example, we observed a significant relative increase in Proteobacteria (Fig. 5A), along with increased Bacteroides, and decreased Firmicutes in mice treated with DSS, compared to untreated control animals (Fig. 5D). Overall, the DSS alone group had the least microbial community diversity measured within a sample as showed in an alpha diversity graph (using Simpson index) (Fig. 5B). Also, as shown in the Venn diagram (Fig 5E) the iMSC and adMSC treated groups shared more OTUs (operational taxonomic units) with the healthy group compared to the DSS only group.

Effects of mesenchymal stem cells (MSC) administration on gut microbiome. Fecal pellets were collected at 2 days after the last MSC injection from control and MSC-treated animals (n = 5 per group) and analyzed by 16s rRNA sequencing as determined as described in “Materials and Methods” section. (A): Relative abundance of Proteobacteria presented in each treatment group. (B): Microbial diversity within treatment groups (Simpson alpha diversity index) calculated with QIIME (Version 1.7.0). (C): Comparative analysis of differences between treatment groups (beta diversity), heat map represents average differences, with 0 as no difference, 0.3 is maximum differences. Scale bar shown in bottom left. (D): Relative abundance of top 10 phyla presented in each treatment group. (E): Venn diagram generated according to the Operational Taxonomic Unit clustering of each treatment group, each number represents number of bacterial species, either shared or unique to treatment groups. The statistics reported as mean ± SD, statistical differences were calculated using One-way ANOVA with Tukey's adjustment (*, p ≤ .05 **, p ≤ .01 ***, p ≤ .001 ****, p ≤ .0001). Abbreviations: adMSC, adipose-derived mesenchymal stem cell DSS, dextran sodium sulfate iMSC, iPSC-derived mesenchymal stem cells.

Notably, in mice treated with either iMSC or adMSC, after 10 days of stem cell treatment, and despite the continued administration of DSS, the composition of the microbiome in these animals had returned to a population that much more closely resembled that of the microbiome of healthy untreated mice (Fig. 5C). For example, the iMSC-treated group of animals had the most similar taxa distribution relative to heathy control animals, compared to DSS only animals or DSS animals treated with adMSC (Fig. 5D). These results suggest that treatment with MSC helped restore the normal colonic flora, although the exact mechanism of the effect remained undetermined.

Effects of MSC Administration on Intestinal Inflammation

The effects of MSC injection on inflammatory responses in the colon and regional lymphoid tissues were examined next. In colonic tissues of DSS-treated animals, increased infiltrates of F4/80+ and CD11b+ macrophages, CD3+, CD4+ T cells, and CD103+ inflammatory monocytes were observed, compared to colonic tissues of healthy untreated animals.

In animals treated with MSC, the numbers of macrophages and monocytes were significantly reduced compared to DSS only treated animals treated (Fig. 6A). FOXP3+ regulatory T cells were significantly more numerous in colonic tissues of mice treated with either iMSC or adMSC, compared to control animals or DSS-only animals (Fig. 6A). These results are consistent with the results of previous studies 13, 22 and suggest that MSC treatment ameliorated colonic inflammation, as reflected by increased numbers of regulatory T cells and reduced macrophage and neutrophil infiltration into colonic submucosa. However, MSC treatment in our model did not alter the numbers of infiltrating T cells in colonic tissues (Fig. 6A).

Effects of mesenchymal stem cells administration on colonic inflammation. Tissue sections were immunostained with the indicated antibodies, as noted in “Materials and Methods” section, and imaged using a confocal microscope. Quantitative image analysis was used to quantitate the density of inflammatory cells (see “Materials and Methods” section). (A): Distribution of FOXP3, F4/80, CD11b, CD3, CD4 T cell, and CD103+ cells in colonic tissues. (B): Leukocyte populations in spleen and mesenteric lymph node tissues as assessed by flow cytometry (see “Materials and Methods” section). Statistical differences were calculated using One-way ANOVA with Dunnett multiple comparison to DSS treated group (*, p ≤ .05 **, p ≤ .01 ***, p ≤ .001 ****, p ≤ .0001). Abbreviations: adMSC, adipose-derived mesenchymal stem cell DAPI, 4′,6-diamidino-2-phenylindole DSS, dextran sodium sulfate FOXP3, Foxhead Box P3 iMSC, iPSC-derived mesenchymal stem cells MLN, mesenteric lymph node Tc, cytotoxic T cell Th, T helper cell Treg, regulatory T cell.

Notably, iMSC and adMSC were comparable in their effects on reducing the severity of colonic inflammation. However, there were no consistent changes in T cell or B cell or myeloid cell populations in the spleen or mesenteric lymph node tissues of animals treated with MSC, compared to animals with DSS-induced colitis (Fig. 6B). Thus, overall administration of MSC reduced local colonic inflammation, in addition to stimulating epithelial cell proliferation and angiogenesis, but had little effect on immune cell populations in extraintestinal tissues.

Trouble shooting advice

Low quality DNA/low yield. There are several possible causes for low yield/quality DNA. The most common reasons include inappropriate storage of sample following collection, non optimal starting amount (too much or too little) of sample collected, and insufficient cell lysis. Suggestion: Reisolate DNA from starting material.

Low/no amplification in PCR (positive controls and standards). There are several possible causing for low/no PCR amplification. Suggestion: Check primer dilutions, you may need to set up fresh working dilutions of primers from oligomer stock.

Polymerase not working efficiently. Suggestion: Check PCR cycling to ensure appropriate activation of DNA polymerase is in place select new aliquot of master mix and repeat PCR.

Fluorescent dye not working. Suggestion: Check PCR machine setting to ensure appropriate detection method for SYBR Green is selected select new aliquot of master mix and repeat PCR.

Low/no amplification in PCR (samples).

low quality DNA (see above)

too much/too little DNA used (see Variation in Copy Number for expected results and modify accordingly)

Amplification in NTC. It is important that you determine the cause of this amplification results from plates with NTC amplification should not be used. Suggestion: First check dissociation curve plot to determine likely causes of amplification in NTC gDNA contamination or primer dimer formation. If cause of amplification in NTC is contamination (usually in the PCR set-up) change water and repeat NTC. Primer dimer formation is another cause of signal in the NTC wells. This amplification can be identified via examination of the dissociation curve primer dimmer dissociation curves will appear very different to telomere dissociation profiles.

SI Text

Expanded Multipressure Hydrodynamic Analysis.

In addition to the experiments examining hydrodynamics of cell/cluster transit at 33-cm H2O pressure in the main text (Fig. 3 B and C), MDA-MB-231 L2 GFP cells were also transited at 83 cm H2O through 5- and 7-µm constrictions for analysis. Cells transiting through 10-µm constrictions under the higher pressure traveled too quickly for accurate analysis using our camera and were not included in this analysis. The dimensionless number, transcapillary conductance (Tc) (derived below), represents the ease at which events are able to traverse a given constriction (higher is greater ease). Tc permits the comparison of the transiting behavior of cells at the different pressures. Single cells transiting at both pressures are plotted in Fig. S2A, demonstrating agreement between the transit behaviors at the different pressures using Tc (Fig. S4). In Fig. S2 B–D, clusters were plotted alongside single-cell data assuming that clusters behaved as (i) cohesive resistive units (total volumes summed), (ii) the largest cell within a given cluster, and (iii) individual cells in series (resistances of constituent cells summed), respectively. Similar to Fig. 3C, clusters plotted as cohesive resistive units (Fig. S2B) overestimated predicted transit conductances. When clusters were plotted as individual cells in series (Fig. S2D), cluster transit most closely matched single-cell data. Plotted as the largest constituent cell (Fig. S2C), clusters underestimated predicted transit conductances. This underestimation is due to numerous cluster events containing high numbers of cells transiting through 7-µm channels at 83 cm H2O (Fig. S3), which skewed the dataset. In the 33-cm H2O pressure case (Fig. 3C), total cluster resistance could be appropriately reduced to the largest cell within the cluster because conductance was highly sensitive to small differences in the sizes of individual cells (Fig. S7). This assumption does not hold true for cases in clusters that contain large numbers of cells with similar sizes. Therefore, in the generalized case, cluster hydrodynamics appears to be most accurately modeled as individual cells transiting in series.

Posttransit Cluster Reorganization and Proliferation.

To study whether the transit process affected the viabilities and proliferative abilities of CTC clusters, cancer cell (Fig. S5) and cultured CTC clusters (Fig. 4A) were observed exiting 7-µm capillary constrictions transiting under 33 cm H2O. Individual cells within CTC clusters rapidly contracted from elongated to rounded morphologies within ∼10–30 s of exiting constrictions (Fig. 4A), and CTC clusters did not experience catastrophic membrane integrity failure during transit as determined by fluorescent dye localization (Fig. 2 A, D, and F, and 4A, Fig. S5, and Movies S2, S3, S8, and S12). Clusters rapidly reformed within into “typical” circular cluster-like morphologies with numerous intercellular adhesions between multiple cells (Fig. S5C). MDA-MB-231-LM2-GFP and LNCaP clusters that traversed constrictions were collected, seeded into fresh media, incubated, and observed over 7 d (Fig. S6). Compared with untreated controls, transited clusters exhibited indistinguishable rates of adhesion and proliferation. The morphologies of transited clusters were also indistinguishable from untreated clusters 1 h after seeding.

Characterization of RBC Transit Morphologies.

The discocytic shapes of RBCs are known to deform in response to the parabolic pressure profiles within capillaries (46). The morphologies of RBCs transiting through engineered capillary constrictions were examined to determine how closely blood cell shapes matched in vivo observations. RBCs transiting under 20-cm H2O pressure, assumed “parachute-like” morphologies in 7-µm constrictions (Movie S1) and “torpedo-like” morphologies in 5-µm constrictions (Movie S2). These shapes were indistinguishable from the shapes of RBCs transiting through 7- and 4-µm-diameter capillaries (46), respectively. This suggests that engineered capillary constrictions were relevant models of the hydrodynamic fluid environment within true capillaries.

Scaling Analysis and Transcapillary Conductance Derivation.

What follows is a highly simplified model of the transit of cells through capillaries. The aim was not to derive an exact law, but to use some simplifications and dimensional arguments to discover important parameters and restrict the functional form of the experimental data.

The model problem was of an elastic spherical cell of radius R forced through a cylindrical capillary of radius r by an applied pressure difference ΔP. The objective was to determine velocity, U, once the cell has fully entered the capillary. Fig. S10A is a definition sketch used for the scaling analysis.

The suspending liquid was taken as a Newtonian fluid with properties similar to water. The scale of the resulting fluid motion was in the regime where the Reynolds number is very small. Because this dimensionless parameter was small, fluid density could be ignored and dynamic viscosity, µ, was the defining fluid parameter (47).

The overall mechanical responses of cells are typically viscoelastic (32). The elastic component of the internal stress of the cell was determined by the instantaneous deformed state whereas the viscous component of the stress was determined by the history of deformation. Cells fully in the constriction transiting at constant velocity were experimentally observed to not further deform. The viscous component of the cell’s mechanical model was therefore assumed to be negligible (in the limit of long channels) when determining the steady transit velocity. The effective elastic modulus, E, was assumed to be the dominant mechanical property. The true mechanical at such large deformations certainly did not follow that of a linearly elastic solid of constant modulus.

In dimensional terms, the steady-state velocity was assumed a function of five parameters: U = f ( R , r , Δ P , μ , E ) , where f is an unknown function, and the exact nature of the elastic modulus is unknown.


Before entering the constriction the spherical cell had total volume, V, given as follows: V = 4 3 π R 3 . Once forced inside the capillary, we assumed that the deformed shape of the cell could be approximated as a cylinder of length L, with a spherical cap of radius r on both ends (Fig. S10A). The volume of this deformed shape was as follows: V = 4 3 π r 3 + π r 2 L . Because cell volume was assumed to be conserved, the dimensionless length of the cylindrical section of the deformed cell was as follows: L r = 4 3 R 3 − r 3 r 3 . From experimental images, this approximation of the true cell shape appeared reasonable (Figs. 1C and 2A). Even if the shape was not precise, the deformation field could be described by a single dimensionless geometric parameter, R/r, the relative size of the cell to the capillary.

Mechanical stress.

The average stress in the cell (σ) was a function of the deformation field and the material property (E). Because the cell was not necessarily a linearly elastic solid, the elastic modulus can be a function of the deformation itself, namely E(R/r). In dimensionless terms, the average stress must follow a form, σ E 0 = ɛ ( E E 0 , R r ) , Where ɛ is an unknown function which depends upon the details of the deformation of the cell and E0 is the small strain constant elastic modulus. The function ɛ incorporates effects due to large deformation or nonlinear elasticity. It is conceivable that the functional form of ɛ may be very different for different types of cells. Although the function ɛ is very difficult to know, the argument here is that, when the deformed shape is constant, σ/E0 is only a function of R/r for a given cell type. In the limit of small deformations, the function ɛ would become the strain.

Fluid lubrication.

As the cell traversed the capillary, we assumed that a thin lubricating fluid layer of thickness, h, was present between the elongated cell and the capillary wall. The viscous shear stress, τ acting on the cell from this lubricating fluid layer was given by classic solution for Couette flow (47): τ = U μ h , where µ is the dynamic viscosity. The total shear force, T, is the stress multiplied by the total area over which it acts, T = 2πrμUL h . In using this model for the viscous stress, we assume the following:

h is a constant across the length of the cylindrical section, or h is an appropriately averaged value.

h « r, the model approximates two sliding parallel plates and do not need to account for the curvature around the capillary.

• Any leak back flow due to the pressure difference from one end of cell to the other is small in terms of the additional viscous stress (47).

When the deformed cell was moving at constant speed, the total shear force must balance the force due to applied pressure, ΔP, across the cell, Δ P π r 2 = 2 π r L μ U h . Rearranging for velocity, U = Δ Pr h 2 μ L . The value of h is unknown and must come from the coupling of the fluid and cell mechanics problem. Although the true value is complex, the order of magnitude of h can be estimated using results from fluid lubrication theory (47). A classic problem in lubrication theory is the slider bearing, where a block slides over a solid surface with a thin fluid gap. The gap height varies linearly over the length of the block. When the block slides at constant speed, the pressure inside the fluid layer increases dramatically and provides a lift force on the block. The excess pressure Pex inside a thin fluid layer can get very high. Although the detailed calculation of the pressure inside the lubrication layer requires knowing the variation in h and thus the shape of the cell in the capillary, the excess pressure in a lubrication layer can be estimated to be on the order of magnitude of the following: P ex ∼ μ U L h 2 . This scaling estimate is similar to a more complete analysis of classic problems for flow of red cells in capillaries (48).

Coupled flow and cell mechanics.

In the coupled problem of cell mechanics and fluid lubrication, the local pressure in the fluid gap must match the local stress of the deformed cell along the length of the cell. We can estimate the scaling of this force balance by equating the average stress and the excess pressure, σ ∼ P ex → ɛ E 0 ∼ μ U L h 2 . Solving for the gap thickness, h 2 ∼ μ U L ɛ E 0 . Combining this expression for the gap height with our expression for cell velocity, we obtain the following: U ∼ Δ P 2 r 2 4 ɛ E 0 μ L , which can be written as follows: U E 0 μ Δ P 2 r ∼ 1 4 ɛ L r . Importantly, everything on the right-hand side of the above equation is a function of R/r only.

Although the function ɛ(R/r) is difficult to know, the magnitude of the strain must provide some measure of right order of magnitude. The end-to-end length of the deformed cell is L + 2r compared with the initial length of 2R. The strain is approximated as follows: ɛ ∼ 2 r + L − 2 R 2 R = 2 R 2 3 r 2 + r 3 R − 1. The true strain for a deformed sphere is a more complicated field, but the above expression is appropriate for an order of magnitude estimate.

Substituting in the expressions for ɛ and our previous expression for L/r and we obtain a dimensionless number we define as transcapillary conductance (Tc): T c = U E μ Δ P 2 d ∼ 1 4 ( 2 R 2 3 r 2 + r 3 R − 1 ) ( 4 3 ( R 3 r 3 − 1 ) ) , which for large values of R/r yields a power law for velocity scaling as (R/r) −5 .

The utility of the above model is not as an exact result, but indicates the scaling law and provides some guidance for plotting the experimental data in dimensionless terms. The model indicates that, for a given fluid, pressure drop, and cell type, Tc can be plotted as a function of the dimensionless cell size R/r (or D/d), T c = U E μ Δ P 2 d . The scaling model points to a few important predictions about the cell velocity, namely, the following:

• Velocity depends strongly on R/r. A power law in the range of (R/r) −5 could reasonably be expected. Stronger dependencies could certainly occur when one considers uncertainty of the mechanics of cells under large deformations.

• Velocity scales as the square of the pressure difference.

• Velocity is inversely proportional to the fluid viscosity and elasticity of the cell i.e., a stiffer cell or more viscous fluid leads to lower velocities.

Finally, we should note that the above analysis assumed that the cell was transiting through a cylindrical tube, whereas in the experiments the cross section of the tube is square. Although we would expect the square shape to influence the details, we do not expect the square channel to change the basic scaling and dimensional analyses presented here. Furthermore, experimental and theoretical data have demonstrated that there are only small differences between pressure signatures CTCs transiting through square and circular cross-section channels (49).

Derivation of Unconstrained Spherical Volumes.

Images of the cell before entrance into the constriction were used to calculate unconstrained cell diameters. Within the entrance region of the devices, cells were constrained in the z axis (vertical) but unconstrained in the x and y axes. Cells/nuclei were assumed to form a cylindrical shape with a “bulge” consisting of a sphere revolved around a radius R′ (Fig. S10B). The radius of the half-sphere, a, was taken as one-half of the channel height, H. From above, cells appeared as disks of apparent radii Γ = R′ + a.

The area of a slice in the z plane is as follows: A = π ( R ′ + x ) 2 = π ( R ′ 2 + 2 R ′ x + x 2 ) . Because x = (a 2 – z 2 ) 1/2 , A ( z ) = π ( R ′ 2 + 2 R ′ a 2 − z 2 + a 2 − z 2 ) . The total volume is as follows: V = ∫ − a a A ( z ) d z = π ∫ − a a ( R ′ 2 + 2 R ′ a 2 − z 2 + a 2 − z 2 ) d z . The first term of the integral simplifies to the following: π ∫ − a a R ′ 2 d z = 2 π R ′ 2 a , which is the volume of the inner solid cylinder of radius R′. The second term in the integral is as follows: π ∫ − a a 2 R ′ a 2 − z 2 d z = π 2 R ′ a 2 . The third term is as follows: π ∫ − a a a 2 − z 2 d z = 4 3 π a 3 . Therefore, the total volume is as follows: V = 2 π R ′ 2 a + π 2 R ′ a 2 + 4 3 π a 3 = 2 π R ′ 2 a ( 1 + π a 2 R ′ + 2 a 2 3 R ′ 2 ) . Note that, if cells/nuclei were simply assumed to take squished cylinder geometries, the computed volumes not have noticeable differences when a/R′ « 1. Experimentally, we measured Γ from the images of the cell in the entrance region and used the known value of a to estimate the cell volumes.

Results and Discussion

We have described three methods for isolating guard cell protoplasts from A. thaliana at small and large scales. Once A. thaliana GCPs are produced, they can be used for dissecting specific stomatal functions that can be investigated using a diversity of assays, such as electrophysiology, biochemistry of signal transduction, and expression profiling of guard cell-expressed genes.

Figure 1a–c shows various stages in the production of A. thaliana GCPs. Figure 1d,f illustrates the high quality of the protoplasts obtained these are round, with intact, evenly distributed chloroplasts. Figure 1d,e illustrates the purity of final large-scale preparations of GCPs and MCPs. Fluorescein dicctate (FDA) staining of the protoplasts obtained by either of the large-scale protocols shows 95% GCP viability. Although all of the results reported here were obtained with the WS ecotype, methods and yields of GCPs were also comparable for the Columbia (0) ecotype (data not shown).

We described two methods for the large-scale isolation of A. thaliana GCPs. The overnight method gives a significantly higher yield of GCPs than the same-day large-scale preparation. This protocol, yields approximately 5 million GCPs, providing approximately 30 µg of soluble and 10 µg of membrane protein we use a minimum of 10 µg protein per lane for Coomassie-stained SDS-PAGE protein gels (Fig. 2). It is possible that the longer the GCPs are exposed to the enzyme solution the greater the impact on the physiology of the protoplasts, although comparable results have been obtained for patch clamping studies using a rapid protoplasting protocol ( Wang et al., 2001 ) or an overnight protoplasting protocol ( Pei et al., 1997 ). Experimental determination of the optimal protoplasting method is suggested for a given assay.

The GCPs isolated by the same-day small-scale method yield ABA-regulated K + and anion currents (Fig. 3) comparable to those described by Pei et al. (1997 ), who used an overnight protoplasting protocol. A major advantage of A. thaliana over other species is the relative ease with which mutants can be identified by both forward and reverse genetic approaches. Schroeder and colleagues have used GCPs from A. thaliana mutants in ABA signaling such as abi1-1, abi2-1, era1, det3 and gca2 to assess resultant alterations in ion channel activity ( Pei et al., 1997, 1998, 2000 Allen et al., 1999, 2000 ). Recently, we have also described alterations in ABA signaling in G protein α subunit (gpa1) mutants ( Wang et al., 2001 ) and Szyroki et al. (2001 ) have described K + currents in a kat1 mutant.

ABA regulation of ion channels is achieved through complex and interwoven signal transduction pathways ( Leung & Giraudat, 1998 Munnik, 2001 ). In Fig. 4b,c we illustrate that, as previously shown for V. faba ( Jacob et al., 1999 ), ABA activates PLD activity in A. thaliana guard cells isolated by same-day large-scale method. It will be of interest to assay the available A. thaliana ABA-related mutants to determine which, if any, are altered in ABA activation of PLD.

Despite the large-scale protocols described here, the amount of GCP isolated is still limiting for RNA gel blot analysis of gene expression. Five million GCPs from the large-scale overnight approach typically yield only enough RNA for two or three lanes on a Northern blot. In contrast, sensitive RT-PCR approaches can provide an assessment of transcript levels starting with much smaller quantities of RNA. We described two RT-PCR-based methods for assessing relative gene expression levels in GCPs vs MCPs. The two approaches should and do yield qualitatively similar results, as illustrated in Fig. 5.

We had previously cloned the cDNA for an ABA-activated protein kinase (AAPK) from V. faba guard cells ( Li et al., 2000 ). Based on sequence analysis, the A. thaliana genome contains several possible AAPK orthologs. Figure 5a shows expression levels for one of these. Expression is significantly higher in GCPs than in MCPs, although there is some expression in the latter cell type, in contrast to V. faba where AAPK expression appears to be limited to guard cells ( Li et al., 2000 ). By contrast, a chloroplastic carbonic anhydrase ( Raines et al., 1992 ), which facilitates CO2–HCO3 − conversion, shows significantly lower expression in GCPs than in MCPs, consistent with the expectation from other plant species that GCPs have a limited photosynthetic capacity ( Goh et al., 1999 ). A comparable result was obtained in preliminary microarray analysis of GCP vs. MCP expressed genes, in which carbonic anhydrase and several other photosynthesis-related genes were observed to be expressed at higher levels in MCPs than in GCPs (S. Pandey and S. M. Assmann, unpubl. data). The RT-PCR of two characterized genes as ‘controls’ also validates our results. Amplification of the KAT1 gene which encodes an inward K + channel (as a guard cell-specific gene control) and actin genes (as a gene showing equal expression in all the cell types) clearly shows a greater abundance of KAT1 transcript in GCPs and approximately equal expression of actin genes in GCPs and MCPs. Such results demonstrate that modified, semiquantitative RT-PCR methods are an efficient method to compare expression profiles of GCPs and MCPs. Because such approaches are rapid, simple, use small quantities of RNA and are cost effective, they can be used in an initial survey of many genes. Once the candidate genes with interesting differences in the expression levels have been identified in this manner, one can proceed to more quantitative but expensive (in terms of reagents or labor) techniques such as real time RT-PCR ( Bustin, 2000 Szyroki et al., 2001 ), RNA gel blot and/or microarrays to elucidate the absolute quantitative difference in the expression levels.

Owing to the presence of mesophyll cells as contaminants and the extreme sensitivity of RT-PCR (compared with nonamplification hybridization techniques such as Northern blots) it is always advisable to compare the expression profile in both the cell types. If a RT-PCR product is observed at a lower cycle number or at a higher dilution in GCPs than in MCPs, then it is logical to conclude that the gene of interest is indeed expressed by the guard cells. One can also keep in mind that mesophyll contamination is a problem mostly with ‘amplification’ techniques such as PCR and RT-PCR, where very low quantities can be amplified to a significant level, whereas for techniques such as Northern blot or SDS-PAGE, where no amplification is involved, 1% MCP contamination does not contribute significantly to the results. Other contaminants, such as broken cell debris, do not contribute to the cDNA, as confirmed by observation of SYTO (Molecular Probes, Eugene, OR, USA) staining of nucleic acids only in intact protoplasts (data not shown). Staining was performed according to the manufacturer’s protocol.

In addition to the assays described above, A. thaliana GCPs have been used in fluorescence assays of cellular processes. Schreiber and colleagues have applied fluorescence transient analysis to assess guard cell photosynthesis ( Goh et al., 1999 ). Since fluorescent indicators for ions, lipids, and other signaling molecules continue to proliferate ( Gilroy, 1997 ), the potential for application to A. thaliana GCPs appears great. Although their small size renders A. thaliana GCPs less likely than GCPs from other species to be practical for assays of protoplast volume change in response to environmental signals, in all other respects, A. thaliana GCPs appear to be a highly useful addition to the plant physiologist’s toolkit.

Disclosure of Potential Conflicts of Interest

O.E.S., M.C., and K.-H.G. are cofounders and owners of Islet One AB, have uncompensated employment, and have compensated intellectual property rights and ownership interest. S.R. is a minority co-owner of Biolamina and has compensated intellectual property rights and ownership interest. J.S. is founder and co-owner of Exosome Diagnostics and has compensated employment, intellectual property rights, and ownership interest. C.R. and T.K. are compensated employees of Exosome Diagnostics. D.J.W. has compensated research funding from Athersys Inc. and United Therapuetics. The other authors indicated no potential conflicts of interest.

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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