Published in Cell Magazine on July 14, 2016.
Live cell extraction and application - live cell extraction at the individual cell level
Due to the heterogeneity of cells, analysis at the single cell level becomes very important. At present, the method for single-cell analysis mainly uses chemical and biological methods to cleave and extract the contents for analysis. However, this method often causes some damage to the sample. Direct extraction of living cells has many advantages, but it is difficult to operate. A new technology report using FluidFM technology is now expected to provide a new and easy way to extract live cells.
1. Why do you need to perform living single cell extraction?
With the development of technology, research on cells has begun to move closer to the analysis of the single-cell domain. With the discovery of cell heterogeneity, people have begun to realize more that even if the genotypes are identical, due to the difference in gene expression, the phenotypes of the cells will be different, resulting in the function, composition, etc. of each cell. Every aspect is not exactly the same. This difference is widespread in the biological world, even in homologous cell populations. Therefore, molecular analysis at the single cell level has become an important means to reveal pathological features, cellular stress and the like.
There are also many challenges in the current single-cell analysis approach. At present, the main method is to sort cells by cell flow cytometry or microfluidic chip and isolate single cells, and then analyze the cells by lysis. Although this high-throughput sorting method is rapid, it itself needs to take the cells out of the original culture environment, which always leads to the loss or damage of some biological information inside the cells. Moreover, since the cell destruction method uses chemical or biological means of lysis, the lysate used also destroys some components in the cell. Therefore, single-cell analysis has always faced significant challenges.
Therefore, scholars began to try to extract their components directly from living cells. A number of different living cell extraction techniques have been established, and the results obtained by such methods are often better than the lysis method. Therefore, living cell extraction technology is a more non-destructive way to obtain intracellular components.
2. Using the FluidFM device to extract cells does not affect cell viability
In this paper, the authors first established a method for extracting cells using FluidFM. They tried various extractions to determine the maximum amount of cells that can be extracted using FluidFM. In the experiment they found that 4 pL of cytoplasm was extracted using FluidFM and observed that 82% of the cells were still alive, as shown in Figure 1B. However, if the extraction is increased to 4.5 pL, no cells can survive. After extracting 0.6 pL from the nucleus, 86% of the cells survived, as shown in Figure 1A. Volume extraction at 0.7 pL or more inevitably leads to cell death, as shown in Figure 1B. They then examined the survival of the cells after extraction. They then continued to observe the extracted cells and found that the function of these cells was not affected. The cells were able to divide normally after about 30 hours after extraction, as shown in Figure 1E. Therefore, the authors believe that the maximum amount of extraction using FluidFM is 0.6 pL of nucleus and 4 pL of cytoplasm.
Figure 1. Cell viability assay A) Volume distribution of native cells, dashed line is the mean, dotted line is the maximum and minimum; B) relationship between cell extraction volume and viable cell count. C) changes in GFP after nuclear extraction from living cells; D) changes in GFP after nuclear extraction from dead cells; E) continuous observation of cell morphology after extraction of live cells (2.9 pL).
3. Three applications using FluidFM single cell extract
3.1 Transmission electron microscopy negative staining observation
3.1 Transmission electron microscopy negative staining observation
Observation of cell substructures is often important for revealing cytopathic effects. However, the traditional means of cell lysis often produces a large amount of debris, so the observation of organelles has caused many difficulties. In this report, the authors extracted the cell contents using a FluidFM device and transferred them to a TEM copper mesh in a low temperature environment, followed by negative staining and evaporation, then placed the slides under transmission electron microscopy and used conventional lysis. The method obtained a single cell solution while laying on a copper grid for comparison. By observing them, it was found that the cell extract obtained by the FluidFM technique was able to observe a large bubble-like structure, a small spherical structure, and a filament-like structure as shown in Fig. 2C. In contrast, the results obtained by cell lysis are not satisfactory, as shown in Figure 2D.
Figure 2. Electron micrograph of cell extract negative staining A) Schematic diagram of electron microscopy sample preparation; B) enlarged view of extracted droplets on electron microscope copper mesh; C) Image of cell extract of electron microscope by FluidFM technology; D) Common lysis method An image of the cell extract under electron microscopy.
3.2 Detection of enzyme activity
The detection of enzyme activity is of great significance for exploring cellular heterogeneity. Therefore, the authors also compared the cell extracts extracted by FluidFM. First, the authors determined the integrity of the extracted protein by beta-galactosidase assay. By measuring the fluorescence intensity of the fluorescein produced by the enzymatic substrate, they successfully observed that the fluorescence intensity increased with time, indicating that the protein in the extract was not destroyed, as shown in Figures 3C and D. Subsequently, the authors tested different enzyme activities on different cells. The results showed that both the LacZ transgenic HeLa cells and the Caspase3 enzyme of HeLa cells were successfully obtained, as shown in Fig. 3 E and F.
Figure 3. Enzyme activity analysis A) Schematic diagram of cell extraction analysis; B) Place the extracted 3 pL cell extract in pre-sealed microwells; C) Enzymatically lysate the substrate to produce fluorescein fluorescence intensity changes, fluorescence Visible after 1 hour. D) Graphic quantification of fluorescence intensity schedule; E) Difference in β-galactosidase activity between LacZ transgenic cells and non-transgenic cells; F) Determination of Caspase 3 enzyme activity.
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4.3 Single cell level transcriptional detection
Gene expression at the single cell level typically requires reverse transcription or PCR amplification followed by qPCR. Before this, it was often necessary to lyse the cells, and the authors used a different strategy than the traditional method. They first created the use of FluidFM to extract approximately 0.01 pg of RNA directly from living cells, and synthesized cDNA using a common PCR tube and performed qPCR assays, as shown in Figure 4A. Since such a small extraction amount cannot be directly placed in the PCR tube, the strategy adopted by them is to first inject the extract into 1 uL of water and then transfer it to the PCR tube for synthesis and detection as shown in Fig. 6B. They tested the expression levels of two housekeeping genes, beta-actin (ACTB) beta-2-microglobulin (B2M) and GFP mRNA. 90% of the 21 samples successfully detected the expression of at least 1 gene, and 2/3 of the samples could simultaneously detect the expression of the three genes. In the detection of the nucleus, expression of at least one gene can also be detected, as shown in Fig. 4C. When the expression of these three genes in the cytoplasm (1.7 pL) and the nucleus (1.3 pL) was simultaneously extracted from the same cell, it was found that the two were substantially identical, as shown in Fig. 4D.
Figure 4 A) Schematic diagram of single cell extraction mRNA transcription assay; B) method of placing extract into droplets; C) ERCC spike as control, determination of Ct values ​​of GFP, B2M, ACTB in cytoplasm; D) on the same cell The cytoplasm and nucleus were extracted and the Ct value was determined.
to sum up
As biological research becomes more microscopic, the need to analyze individual cells is growing. However, due to the small size of a single cell, the amount of material that can be extracted is significantly more difficult than previous cell population analysis. This not only has new and higher requirements for the sensitivity of the test instrument, but also raises a higher index for the quality of the sample itself. In this article, the quality of the sample obtained by using FluidFM to extract living cells is significantly improved compared with the conventional pyrolysis method, and satisfactory results have been obtained. In addition, after this method controls the amount of extraction, it is even possible to complete the extraction without killing the cells, which makes it possible to trace the single cell metabolism assay.
Multifunctional single cell microscopy operating system - Cytosurge FluidFM BOT, Switzerland
The versatile single-cell microscopy operating system, FluidFM BOT, is a single-cell operating system that integrates an atomic force system, a microfluidic system, and a cell culture system. The main functions include single cell injection, single cell extraction, and single cell separation.
FluidFM BOT greatly facilitates single-cell level studies, especially for precision medicine, single-cell biology, single-cell mass spectrometry, single-cell gene editing, and drug discovery.
Single cell manipulation solution for injection, extraction and sorting
references:
1. Actis, P., Maalouf, MM, Kim, HJ, Lohith, A., Vilozny, B., Seger, RA, and Pourmand, N. (2014). Compartmental genomics in living cells revealed by single-cell nanobiopsy. ACS Nano 8, 546–553.
2. Amara, A., and Mercer, J. (2015). Viral apoptotic mimicry. Nat. Rev. Microbiol. 13, 461–469.
3. Bengtsson, M., Sta° hlberg, A., Rorsman, P., and Kubista, M. (2005). Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15, 1388–1392.
4. Bertrand, R., Solary, E., O'Connor, P., Kohn, KW, and Pommier, Y. (1994). Induction of a common pathway of apoptosis by staurosporine. Exp. Cell Res. 211, 314 –321.
5. Cai, X., Evrony, GD, Lehmann, HS, Elhosary, PC, Mehta, BK, Poduri, A., and Walsh, CA (2014). Single-cell, genome-wide sequencing identification clonal somatic copy-number Variation in the human brain. Cell Rep. 8, 1280–1289.
6. Grindberg, RV, Yee-Greenbaum, JL, McConnell, MJ, Novotny, M., O'Shaughnessy, AL, Lambert, GM, Arau Ì zo-Bravo, MJ, Lee, J., Fishman, M., Robbins , GE, et al. (2013). RNA-sequencing from single nuclei. Proc. Natl. Acad. Sci. USA 110, 19802–19807.
7. Guillaume-Gentil, O., Potthoff, E., Ossola, D., Do ̈ rig, P., Zambelli, T., and Vorholt, JA (2013). Force-controlled fluidic injection into single cell nuclei. Small 9, 1904–1907.
8. Guillaume-Gentil, O., Potthoff, E., Ossola, D., Franz, CM, Zambelli, T., and Vorholt, JA (2014). Force-controlled manipulation of single cells: from AFM to FluidFM. Trends Biotechnol. 32, 381–388.
9. Hashimshony, T., Wagner, F., Sher, N., and Yanai, I. (2012). CEL-Seq: singlecell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673.
10. Jiang, L., Schlesinger, F., Davis, CA, Zhang, Y., Li, R., Salit, M., Gingeras, TR, and Oliver, B. (2011). Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551.
11. Kovarik, ML, and Allbritton, NL (2011). Measuring enzyme activity in single cells. Trends Biotechnol. 29, 222–230.
12. Kuipers, MA, Stasevich, TJ, Sasaki, T., Wilson, KA, Hazelwood, KL, McNally, JG, Davidson, MW, and Gilbert, DM (2011). Highly stable loading of Mcm proteins onto chromatin in living cells Requires replication to unload. J. Cell Biol. 192, 29–41.
13. Liebherr, RB, Hutterer, A., Mickert, MJ, Vogl, FC, Beutner, A., Lechner, A., Hummel, H., and Gorris, HH (2015). Three-in-one enzyme assay based On single molecule detection in femtoliter arrays. Anal. Bioanal. Chem. 407, 7443–7452.
14. Lo, SJ, and Yao, DJ (2015). Get to understand more from single-cells: current studies of microfluidic-based techniques for single-cell analysis. Int. J. Mol. Sci. 16, 16763–16777.
15. Meister, A., Gabi, M., Behr, P., Studer, P., Vo ̈ ro ̈ s, J., Niedermann, P., Bitterli, J., Polesel-Maris, J., Liley, M., Heinzelmann, H., and Zambelli, T. (2009). FluidFM: combining atomic force microscopy and nanofluidics in a universal liquid delivery system for single cell applications and beyond. Nano Lett. 9, 2501–2507.
16. Nagaraj, N., Wisniewski, JR, Geiger, T., Cox, J., Kircher, M., Kelso, J., Pa ̈ a ̈ bo, S., and Mann, M. (2011). Deep Proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7, 548.
17. Nawarathna, D., Turan, T., and Wickramasinghe, HK (2009). Selective probing of mRNA expression levels within a living cell. Appl. Phys. Lett. 95, 83117. O'Huallachain, M., Karczewski, KJ, Weissman, SM, Urban, AE, and Snyder, MP (2012). Extensive genetic variation in somatic human tissues. Proc. Natl. Acad. Sci. USA 109, 18018–18023.
18. Osada, T., Uehara, H., Kim, H., and Ikai, A. (2003). mRNA analysis of single living cells. J. Nanobiotechnology 1, 2.
19. Pfeiffer-Guglielmi, B., Dombert, B., Jablonka, S., Hausherr, V., van Thriel, C., Scho ̈ bel, N., and Jansen, RP (2014). Axonal and dendritic localization of mRNAs for glycogen-metabolizing enzymes in cultured rodent neurons. BMC Neurosci. 15, 70.
20. Picelli, S., Faridani, OR, Bjo ̈ rklund, AK, Winberg, G., Sagasser, S., and Sandberg, R. (2014). Full-length RNA-seq from single cells using Smartseq2. Nat. Protoc. 9, 171–181.
21. Raj, A., Peskin, CS, Tranchina, D., Vargas, DY, and Tyagi, S. (2006). Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309.
22. Ramsko ̈ ld, D., Luo, S., Wang, YC, Li, R., Deng, Q., Faridani, OR, Daniels, GA, Khrebtukova, I., Loring, JF, Laurent, LC, et Al. (2012). Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782.
23. Rissin, DM, Kan, CW, Campbell, TG, Howes, SC, Fournier, DR, Song, L., Piech, T., Patel, PP, Chang, L., Rivnak, AJ, et al. (2010 Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. Nat. Biotechnol. 28, 595–599.
24. Rondelez, Y., Tresset, G., Tabata, KV, Arata, H., Fujita, H., Takeuchi, S., and Noji, H. (2005). Microfabricated arrays of femtoliter chambers allow single molecule enzymology. Nat. Biotechnol. 23, 361–365.
25. Saha-Shah, A., Weber, AE, Karty, JA, Ray, SJ, Hieftje, GM, and Baker, LA (2015). Nanopipettes: probes for local sample analysis. Chem. Sci. 6, 3334–3341 .
26. Sarkar, A., Kolitz, S., Lauffenburger, DA, and Han, J. (2014). Microfluidic probe for single-cell analysis in adherent tissue culture. Nat. Commun. 5, 3421.
27. Schmid, A., Kortmann, H., Dittrich, PS, and Blank, LM (2010). Chemical and biological single cell analysis. Curr. Opin. Biotechnol. 21, 12–20.
28. Tang, F., Barbacioru, C., Nordman, E., Li, B., Xu, N., Bashkirov, VI, Lao, K., and Surani, MA (2010). RNA-Seq analysis to capture The transcriptome landscape of a single cell. Nat. Protoc. 5, 516–535.
29. Taniguchi, K., Kajiyama, T., and Kambara, H. (2009). Quantitative analysis of gene expression in a single cell by qPCR. Nat. Methods 6, 503–506.
30. Van Gelder, RN, von Zastrow, ME, Yool, A., Dement, WC, Barchas, JD, and Eberwine, JH (1990). Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc. Natl. Acad. Sci USA 87, 1663–1667.
31. Veyer, DL, Maluquer de Motes, C., Sumner, RP, Ludwig, L., Johnson, BF, and Smith, GL (2014). Analysis of the anti-apoptotic activity of four vaccinia virus proteins demonstrates that B13 is The most potent inhibitor in isolation and during viral infection. J. Gen. Virol. 95, 2757–2768.
32. Wachsmuth, M., Weidemann, T., Mu ̈ ller, G., Hoffmann-Rohrer, UW, Knoch, TA, Waldeck, W., and Langowski, J. (2003). Analyzing intracellular binding and diffusion with continuous Fluorescence photobleaching. Biophys. J. 84, 3353–3363.
33. Wang, D., and Bodovitz, S. (2010). Single cell analysis: the new frontier in 'omics'. Trends Biotechnol. 28, 281–290.
34. Wasilenko, ST, Stewart, TL, Meyers, AF, and Barry, M. (2003). Vaccinia virus encodes a previously uncharacterized mitochondrial-associated inhibitor of apoptosis. Proc. Natl. Acad. Sci. USA 100, 14345–14350 .
35. Weis, K. (2003). Regulating access to the genome: nucleocytoplasmic transport throughout the cell cycle. Cell 112, 441–451.
36. Wu, M., and Singh, AK (2012). Single-cell protein analysis. Curr. Opin. Biotechnol.23, 83–88.
37. Zhao, L., Kroenke, CD, Song, J., Piwnica-Worms, D., Ackerman, JJ, and Neil, JJ (2008). Intracellular water-specific MR of microbead-adherent cells: the HeLa cell intracellular Water exchange lifetime. NMR Biomed. 21, 159–164.
Related products and links: 1. Actis, P., Maalouf, MM, Kim, HJ, Lohith, A., Vilozny, B., Seger, RA, and Pourmand, N. (2014). Compartmental genomics in living cells revealed by single-cell nanobiopsy. ACS Nano 8, 546–553.
2. Amara, A., and Mercer, J. (2015). Viral apoptotic mimicry. Nat. Rev. Microbiol. 13, 461–469.
3. Bengtsson, M., Sta° hlberg, A., Rorsman, P., and Kubista, M. (2005). Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15, 1388–1392.
4. Bertrand, R., Solary, E., O'Connor, P., Kohn, KW, and Pommier, Y. (1994). Induction of a common pathway of apoptosis by staurosporine. Exp. Cell Res. 211, 314 –321.
5. Cai, X., Evrony, GD, Lehmann, HS, Elhosary, PC, Mehta, BK, Poduri, A., and Walsh, CA (2014). Single-cell, genome-wide sequencing identification clonal somatic copy-number Variation in the human brain. Cell Rep. 8, 1280–1289.
6. Grindberg, RV, Yee-Greenbaum, JL, McConnell, MJ, Novotny, M., O'Shaughnessy, AL, Lambert, GM, Arau Ì zo-Bravo, MJ, Lee, J., Fishman, M., Robbins , GE, et al. (2013). RNA-sequencing from single nuclei. Proc. Natl. Acad. Sci. USA 110, 19802–19807.
7. Guillaume-Gentil, O., Potthoff, E., Ossola, D., Do ̈ rig, P., Zambelli, T., and Vorholt, JA (2013). Force-controlled fluidic injection into single cell nuclei. Small 9, 1904–1907.
8. Guillaume-Gentil, O., Potthoff, E., Ossola, D., Franz, CM, Zambelli, T., and Vorholt, JA (2014). Force-controlled manipulation of single cells: from AFM to FluidFM. Trends Biotechnol. 32, 381–388.
9. Hashimshony, T., Wagner, F., Sher, N., and Yanai, I. (2012). CEL-Seq: singlecell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673.
10. Jiang, L., Schlesinger, F., Davis, CA, Zhang, Y., Li, R., Salit, M., Gingeras, TR, and Oliver, B. (2011). Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551.
11. Kovarik, ML, and Allbritton, NL (2011). Measuring enzyme activity in single cells. Trends Biotechnol. 29, 222–230.
12. Kuipers, MA, Stasevich, TJ, Sasaki, T., Wilson, KA, Hazelwood, KL, McNally, JG, Davidson, MW, and Gilbert, DM (2011). Highly stable loading of Mcm proteins onto chromatin in living cells Requires replication to unload. J. Cell Biol. 192, 29–41.
13. Liebherr, RB, Hutterer, A., Mickert, MJ, Vogl, FC, Beutner, A., Lechner, A., Hummel, H., and Gorris, HH (2015). Three-in-one enzyme assay based On single molecule detection in femtoliter arrays. Anal. Bioanal. Chem. 407, 7443–7452.
14. Lo, SJ, and Yao, DJ (2015). Get to understand more from single-cells: current studies of microfluidic-based techniques for single-cell analysis. Int. J. Mol. Sci. 16, 16763–16777.
15. Meister, A., Gabi, M., Behr, P., Studer, P., Vo ̈ ro ̈ s, J., Niedermann, P., Bitterli, J., Polesel-Maris, J., Liley, M., Heinzelmann, H., and Zambelli, T. (2009). FluidFM: combining atomic force microscopy and nanofluidics in a universal liquid delivery system for single cell applications and beyond. Nano Lett. 9, 2501–2507.
16. Nagaraj, N., Wisniewski, JR, Geiger, T., Cox, J., Kircher, M., Kelso, J., Pa ̈ a ̈ bo, S., and Mann, M. (2011). Deep Proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7, 548.
17. Nawarathna, D., Turan, T., and Wickramasinghe, HK (2009). Selective probing of mRNA expression levels within a living cell. Appl. Phys. Lett. 95, 83117. O'Huallachain, M., Karczewski, KJ, Weissman, SM, Urban, AE, and Snyder, MP (2012). Extensive genetic variation in somatic human tissues. Proc. Natl. Acad. Sci. USA 109, 18018–18023.
18. Osada, T., Uehara, H., Kim, H., and Ikai, A. (2003). mRNA analysis of single living cells. J. Nanobiotechnology 1, 2.
19. Pfeiffer-Guglielmi, B., Dombert, B., Jablonka, S., Hausherr, V., van Thriel, C., Scho ̈ bel, N., and Jansen, RP (2014). Axonal and dendritic localization of mRNAs for glycogen-metabolizing enzymes in cultured rodent neurons. BMC Neurosci. 15, 70.
20. Picelli, S., Faridani, OR, Bjo ̈ rklund, AK, Winberg, G., Sagasser, S., and Sandberg, R. (2014). Full-length RNA-seq from single cells using Smartseq2. Nat. Protoc. 9, 171–181.
21. Raj, A., Peskin, CS, Tranchina, D., Vargas, DY, and Tyagi, S. (2006). Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309.
22. Ramsko ̈ ld, D., Luo, S., Wang, YC, Li, R., Deng, Q., Faridani, OR, Daniels, GA, Khrebtukova, I., Loring, JF, Laurent, LC, et Al. (2012). Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782.
23. Rissin, DM, Kan, CW, Campbell, TG, Howes, SC, Fournier, DR, Song, L., Piech, T., Patel, PP, Chang, L., Rivnak, AJ, et al. (2010 Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. Nat. Biotechnol. 28, 595–599.
24. Rondelez, Y., Tresset, G., Tabata, KV, Arata, H., Fujita, H., Takeuchi, S., and Noji, H. (2005). Microfabricated arrays of femtoliter chambers allow single molecule enzymology. Nat. Biotechnol. 23, 361–365.
25. Saha-Shah, A., Weber, AE, Karty, JA, Ray, SJ, Hieftje, GM, and Baker, LA (2015). Nanopipettes: probes for local sample analysis. Chem. Sci. 6, 3334–3341 .
26. Sarkar, A., Kolitz, S., Lauffenburger, DA, and Han, J. (2014). Microfluidic probe for single-cell analysis in adherent tissue culture. Nat. Commun. 5, 3421.
27. Schmid, A., Kortmann, H., Dittrich, PS, and Blank, LM (2010). Chemical and biological single cell analysis. Curr. Opin. Biotechnol. 21, 12–20.
28. Tang, F., Barbacioru, C., Nordman, E., Li, B., Xu, N., Bashkirov, VI, Lao, K., and Surani, MA (2010). RNA-Seq analysis to capture The transcriptome landscape of a single cell. Nat. Protoc. 5, 516–535.
29. Taniguchi, K., Kajiyama, T., and Kambara, H. (2009). Quantitative analysis of gene expression in a single cell by qPCR. Nat. Methods 6, 503–506.
30. Van Gelder, RN, von Zastrow, ME, Yool, A., Dement, WC, Barchas, JD, and Eberwine, JH (1990). Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc. Natl. Acad. Sci USA 87, 1663–1667.
31. Veyer, DL, Maluquer de Motes, C., Sumner, RP, Ludwig, L., Johnson, BF, and Smith, GL (2014). Analysis of the anti-apoptotic activity of four vaccinia virus proteins demonstrates that B13 is The most potent inhibitor in isolation and during viral infection. J. Gen. Virol. 95, 2757–2768.
32. Wachsmuth, M., Weidemann, T., Mu ̈ ller, G., Hoffmann-Rohrer, UW, Knoch, TA, Waldeck, W., and Langowski, J. (2003). Analyzing intracellular binding and diffusion with continuous Fluorescence photobleaching. Biophys. J. 84, 3353–3363.
33. Wang, D., and Bodovitz, S. (2010). Single cell analysis: the new frontier in 'omics'. Trends Biotechnol. 28, 281–290.
34. Wasilenko, ST, Stewart, TL, Meyers, AF, and Barry, M. (2003). Vaccinia virus encodes a previously uncharacterized mitochondrial-associated inhibitor of apoptosis. Proc. Natl. Acad. Sci. USA 100, 14345–14350 .
35. Weis, K. (2003). Regulating access to the genome: nucleocytoplasmic transport throughout the cell cycle. Cell 112, 441–451.
36. Wu, M., and Singh, AK (2012). Single-cell protein analysis. Curr. Opin. Biotechnol.23, 83–88.
37. Zhao, L., Kroenke, CD, Song, J., Piwnica-Worms, D., Ackerman, JJ, and Neil, JJ (2008). Intracellular water-specific MR of microbead-adherent cells: the HeLa cell intracellular Water exchange lifetime. NMR Biomed. 21, 159–164.
Swiss Cytosurge FluidFM BOT multi-function single-cell microscopy operating system: http://?id=469
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