1. What you see here is the average face of the average person currently living on planet earth. By averaging we lose all the little details that make us unique.
Our beauty is in our difference and this is true at all levels: We are different at all scales! . From our faces, our minds, our proteins down to individual cells that make up our body even down to single molecules.
National Geographic

2. Our beauty is in our differences
Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

3. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

4. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

5. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

6. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

7. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

8. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

9. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

10. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

11. Our beauty is in our difference and this is true down to level of individual cells and individual molecules.

12. School of Electronic and Electrical Engineering
Single-Cell Genomics
Dr Paolo Actis
School of Electronic and Electrical Engineering
@paoloactis

13. Single-Cell Genomics
Single-cell genome sequencing: current state of the science, Nat Rev Genetics 2016
Single Cell Genomics: Advances and Future Perspectives, PLOS Genetics, 2014

14. Single-Cell Genomics
1. Cell Isolation

15. Single-Cell Genomics
1. Cell Isolation
2. Genome Amplification

16. Single-Cell Genomics 1. Cell Isolation 2. Genome Amplification
3. Genome Sequencing

17. Single-Cell Genomics 1. Cell Isolation 2. Genome Amplification
3. Genome Sequencing
4. Bioinformatics analysis

18. 1. Cell Isolation

19. Cell Isolation Manual Micropipetting
✔Visual confirmation, Applicable to low number of cells
✖Low throughput, Operator Bias

20. Cell Isolation FACS (fluorescence activated cell sorting)
✔High Throughput , Sorting based on phenotype
✖Large amount of cells required, Putative damage to cells,
Occasionally more than one cell isolated

21. Cell Isolation Microfluidics
✔High Throughput , highly standardized, nanoliter reaction volumes, automated with visual confirmation
✖Putative loss of cells

22. Cell Isolation Single nuclei sorting
✔No need to isolate whole cell, isolation form cryopreserved or fresh tissues
✖Potential loss of micronuclei

23. Cell Isolation Laser Capture Microdissection
✔Retains topological information of the cell, isolation form cryopreserved or fresh tissues
✖Contamination with other cells, Potential loss of cellular material

24. Single-Cell Nanobiopsy

25. Electrowetting in a nanopipette
H2O
DCE

26. Electrowetting in a nanopipette

27. Single-Cell Nanobiopsy

28. Single-Cell Nanobiopsy
Actis et al, ACS Nano, 2013

29. RNA-Sequencing High read coverage of RNA sequencing of nanobiopsies
Full length cDNA from transcripts isolated by nanobiopsy

30. Organelle Surgery

31. Single-Cell Nanobiopsy

32. Single-Cell Nanobiopsy

33. Sub-Cellular Sequencing

34. Sub-Cellular Sequencing

35. Sub-Cellular Sequencing

36. Ambition
Nucleolar trafficking during virus infection
(Ade Whitehouse, FBS)
Sub-cellular Sequencing
(Thierry Voet, Sanger Institute)
Omics analysis
(You)

37. 2. Genome Amplification

38. Genome Amplification

39. Genome Amplification
High Uniformity
but
High error rates

40. Genome Amplification

41. Genome Amplification
Low Uniformity
but
Low error rates

42. Genome Amplification

43. intermediate error rates
Genome Amplification
High Uniformity
but
intermediate error rates

44. 3. Genome Sequencing

45. Genome Sequencing
Easy …just ask Ian Carr

47. 4. Bioinformatics analysis

49. Amplification artefacts
Loss of coverage
Amplification artefacts

51. False positive
True positive
False Negative
(locus dropout)

52. Single-Cell Genomics 1. Cell Isolation 2. Genome Amplification
3. Genome Sequencing
4. Bioinformatics analysis

53. Our beauty is in our differences
Dr Paolo Actis
School of Electronic and Electrical Engineering
@paoloactis
Our beauty is in our difference and this is true down to level of individual cells and individual molecules.