1. Quantitative PCR Analysis of DNA, RNAs, and Proteins in the Same Single Cell A. Ståhlberg, C. Thomsen, D. Ruff, and P. Åman December 2012 http://www.clinchem.org/content/58/12/1682.full © Copyright 2012 by the American Association for Clinical Chemistry

2. © Copyright 2009 by the American Association for Clinical Chemistry Introduction  The added value of single-cell analysis  Allow studies between different cell types/subpopulations in a mixed biological sample  Allow heterogeneity studies within a specific cell type  Allow detection and studies of rare cells >Examples of single-cell applications - Characterization of cell fates in stem cell differentiation - Identification and characterization of circulating tumor cells - Genotype and phenotype characterization of tumor cells

3. © Copyright 2009 by the American Association for Clinical Chemistry Introduction  Single-cell techniques (examples)  Next generation sequencing  Flow cytometry  Mass spectrometry  Fluorescence in situ hybridization techniques  Microarrays >Quantitative real-time PCR - Mainstream technology with many users - Broad acceptance and know how in the scientific community - Can be combined with other methods - Many applications See accompanying Editorial: S. Darmanis, C. Gallant, and U. Landegren. PCR-Based Multiparametric Assays in Single Cells. Clin Chem 2012;58:1618-19.

4. © Copyright 2009 by the American Association for Clinical Chemistry Question 1  What are the pros and cons with current single-cell techniques?

5. © Copyright 2009 by the American Association for Clinical Chemistry Materials and Methods  Samples - Human fibrosarcoma cell line HT1080 - Ectopic expression of green fluorescent protein tagged fused in sarcoma (FUS-GFP) using transient transfection  Cell collection - Cell dissociation with trypsin treatment - Cell collection using FACS  Purification-free protocol - Analyte-releasing buffer - Efficient lysis of all analytes without inhibition (see supplement for details)  Sample split - PLA and RT require different running conditions

6. © Copyright 2009 by the American Association for Clinical Chemistry Figure 1. Experimental setup. Overview of experimental work flow for measuring DNA, RNAs, and proteins in the same single cell. We used 40% of each cell for protein analysis, 40% for RNA analysis, and 20% for DNA analysis. Experimental setup

7. © Copyright 2009 by the American Association for Clinical Chemistry Question 2  What are the bottlenecks of current experimental workflow?

8. © Copyright 2009 by the American Association for Clinical Chemistry Results  RNA and DNA assay performance  All assays were specific and sensitive to detect all analytes at single-cell level, theoretically at single molecule level  In principle, unlimited dynamic range  Protein assay performance  Specific and sensitive to detect proteins at single-cell level  Dynamic range: ~3 orders of magnitude  Generate background signal due to unspecific ligation

9. © Copyright 2009 by the American Association for Clinical Chemistry Figure 3. Analysis of protein concentrations in single cells with variable FUS-GFP protein production. (A), Individual cells with different levels of GFP fluorescence (no, low, intermediate, high) measured and sorted by FACS. (B), PLA-qPCR analysis of FUS-GFP protein concentrations correlated with GFP fluorescence measured by FACS (Spearman correlation coefficient, 0.86; P < 0.001). Note that some cells with no GFP fluorescence measured by FACS showed FUS-GFP protein production above that of the negative protein control (NPC). Validation of PLA-qPCR using FACS

10. © Copyright 2009 by the American Association for Clinical Chemistry Spearman correlations between all analytes for all single cells with increasing GFP fluorescence intensity Table 1. Statistically significant (P < 0.01, and P < 0.05) Spearman correlation coefficients are marked (* and **, respectively). FUS-GFP DNA indicates FUS-GFP – encoding DNA; FUS- GFP RNA indicates FUS-GFP – encoding RNA. CCND1, cyclin D1; FUS, fused in sarcoma; MIR31, microRNA 31; SNORD48, small nucleolar RNA, C/D box 48 FUS-GFP protein FUS-GFP DNA FUS-GFP mRNA FUS DNA/RNA CCND1 mRNA MIR31 miRNA SNORD48 ncRNA FUS-GFP protein 1 FUS-GFP DNA 0.37**1 FUS-GFP mRNA 0.31**0.41**1 FUS DNA/RNA 0.36**0.44**0.92**1 CCND1 mRNA -0.11-0.29*-0.160.101 MIR31 miRNA -0.10-0.110.020.170.59**1 SNORD48 ncRNA -0.10-0.210.070.24*0.71**0.66**1 Moderate correlations between FUS-GFP DNA, mRNA and protein FUS is a transcription factor. CCND1, MIR31 and SNORD48 are potential downstream targets of FUS

11. © Copyright 2009 by the American Association for Clinical Chemistry Figure 4. The effect of ectopic FUS expression. CCND1 (green), miR31 (red) and SNORD48 (blue) correlated with (A) endogenous FUS (black), but not (B) ectopic expressed FUS (black). Assuming ergodicity, distributions at a given time point can be seen as expression over time. Positive correlation between RNAs may, for example, be explained by common transcriptional regulation. Question 3  How can correlations between analytes be used to address biological questions?

12. © Copyright 2009 by the American Association for Clinical Chemistry Conclusions  DNA, RNAs, and proteins can be accurately measured in the same single cell using quantitative PCR.  The described method is compatible with most cell-sampling approaches, and generates output for the same parameter for all measured analytes, a feature facilitating comparative data analysis.  Analysis of multiple analytes in the same single cell open up new possibilities for detailed correlations studies.

13. © Copyright 2009 by the American Association for Clinical Chemistry Thank you for participating in this month’s Clinical Chemistry Journal Club. Additional Journal Clubs are available at www.clinchem.org Follow us