Relaxation of the protein electron spin is an important parameter to characterize the environment,
including information on molecular dynamics.
Here, we deduced the longitudinal relaxation time
of the spin label from Fig. 4B. The red circles denote the interaction signal between the NV center
and the spin label; the black dots show the NV
center decoherence curve without operation on the
spin label. Simulation (solid curves) shows a relaxation time of 4 ms. These values are compatible
with those for spin labels in ensemble measurements,
as the relaxation time of this kind of spin label
is ~110 ms at liquid nitrogen temperature (21, 24).
The ability to address single-electron spin labels on proteins adds another element to the
emerging diamond sensor–based toolbox for ultra-precise structure determination. Together with
the recently established nuclear magnetic resonance (NMR) detection, the present method extends the sensing range to dozens of nanometers,
whereas diamond sensor–based NMR only senses
nuclear spins in very close proximity (a few nanometers) to the NV center (26–29). The interaction between the spin label and the neighboring
nuclei could be used to sense more distant nuclei
and provide structural and dynamical information otherwise inaccessible by the sensor. In this
respect, it is particularly encouraging that we
find long spin relaxation times enabling coherent spin driving at the protein. This capability
will allow the use of the ancillary electron spin
for sophisticated coherent control (30, 31), thereby facilitating future polarization transfer experiments that could gain access to nuclear spins in
proteins, including proton or 13C spins. When
combined with either scanning magnetometry
or nanoscale magnetic resonance imaging based
on magnetic field gradients, protein structure
analysis under ambient conditions at the level of
a single molecule is within reach (32, 33).
REFERENCES AND NOTES
1. L. Redecke et al., Science 339, 227–230 (2013).
2. T. R. M. Barends et al., Nature 505, 244–247 (2014).
3. M. C. Scott et al., Nature 483, 444–447 (2012).
4. C. C. Chen et al., Nature 496, 74–77 (2013).
5. P. P. Borbat, A. J. Costa-Filho, K. A. Earle, J. K. Moscicki,
J. H. Freed, Science 291, 266–269 (2001).
6. D. Rugar, R. Budakian, H. J. Mamin, B. W. Chui, . Nature 430,
7. M. Xiao, I. Martin, E. Yablonovitch, H. W. Jiang, Nature 430,
8. Y. Manassen, R. J. Hamers, J. E. Demuth, A. J. Castellano Jr.,
Phys. Rev. Lett. 62, 2531–2534 (1989).
9. G. Balasubramanian et al., Nature 455, 648–651 (2008).
10. J. R. Maze et al., Nature 455, 644–647 (2008).
11. J. M. Taylor et al., Nat. Phys. 4, 810–816 (2008).
12. G. Balasubramanian et al., Nat. Mater. 8, 383–387 (2009).
13. N. Bar-Gill, L. M. Pham, A. Jarmola, D. Budker, R. L. Walsworth,
Nat. Commun. 4, 1743 (2013).
14. M. S. Grinolds et al., Nat. Phys. 9, 215–219 (2013).
15. B. Grotz et al., New J. Phys. 13, 055004 (2011).
16. A. O. Sushkov et al., Nano Lett. 14, 6443–6448
17. L. S. Michel et al., Nature 409, 355–359 (2001).
18. S. Martin-Lluesma, V. M. Stucke, E. A. Nigg, Science 297,
19. M. W. Doherty et al., Phys. Rep. 528, 1–45 (2013).
20. F. Z. Shi et al., Phys. Rev. B 87, 195414 (2013).
21. See supplementary materials on Science Online.
22. V. Gaponenko et al., Protein Sci. 9, 302–309 (2000).
23. J. A. Weil, J. R. Bolten, Electron Paramagnetic Resonance:
Elementary Theory and Practical Applications (Wiley, New York,
ed. 2, 2007), pp. 316–317.
24. K. Jacobsen, S. Oga, W. L. Hubbell, T. Risse, Biophys. J. 88,
25. M. A. Hemminga, L. J. Berliner, ESR Spectroscopy in Membrane
Biophysics (Springer Science and Business Media, New York,
2007), pp. 133–134.
26. T. Staudacher et al., Science 339, 561–563 (2013).
27. H. J. Mamin et al., Science 339, 557–560 (2013).
28. F. Z. Shi et al., Nat. Phys. 10, 21–25 (2014).
29. C. Müller et al., Nat. Commun. 5, 4703 (2014).
30. M. Schaffry, E. M. Gauger, J. J. L. Morton, S. C. Benjamin,
Phys. Rev. Lett. 107, 207210 (2011).
31. A. O. Sushkov et al., Phys. Rev. Lett. 113, 197601 (2014).
32. M. S. Grinolds et al., Nat. Nanotechnol. 9, 279–284 (2014).
33. L. Luan et al., http://arxiv.org/abs/1409.5418 (2014).
We thank F. Jelezko for helpful discussions. Supported by 973
Program grants 2013CB921800 and 2012CB917202, National
Natural Science Foundation of China grants 11227901, 91021005,
31470835, 11275183, and 21103199, and the Chinese Academy
of Sciences. J. W. was supported by the Max Planck Society and
the European Union (via the ERC grants SQUTEC and DIADEMS)
and by the Baden-Württemberg Stiftung.
Materials and Methods
Tables S1 and S2
Figs. S1 to S7
3 November 2014; accepted 29 January 2015
Cell types in the mouse cortex and
hippocampus revealed by
Amit Zeisel,1 Ana B. Muñoz-Manchado,1 Simone Codeluppi,1 Peter Lönnerberg,1
Gioele La Manno,1 Anna Juréus,1 Sueli Marques,1 Hermany Munguba,1 Liqun He,2
Christer Betsholtz,2,3 Charlotte Rolny,4 Gonçalo Castelo-Branco,1
Jens Hjerling-Leffler,1† Sten Linnarsson1†
The mammalian cerebral cortex supports cognitive functions such as sensorimotor integration,
memory, and social behaviors. Normal brain function relies on a diverse set of differentiated
cell types, including neurons, glia, and vasculature. Here, we have used large-scale single-cell RNA
sequencing (RNA-seq) to classify cells in the mouse somatosensory cortex and hippocampal CA1
region. We found 47 molecularly distinct subclasses, comprising all known major cell types in the
cortex. We identified numerous marker genes, which allowed alignment with known cell types,
morphology, and location. We found a layer I interneuron expressing Pax6 and a distinct
postmitotic oligodendrocyte subclass marked by Itpr2. Across the diversity of cortical cell types,
transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the
maintenance of adult cell type identity.
The brain is built from a large number of specialized cell types, enabling highly re- fined electrophysiological behavior, as well as fulfilling brain nutrient needs and defense against pathogens. Functional specialization allows fine-tuning of circuit dynamics and decou- pling of support functions such as energy supply, waste removal, and immune defense. Cells in the nervous system have historically been classified using location, morphology, target specificity, and
1138 6 MARCH 2015 • VOL 347 ISSUE 6226 sciencemag.org SCIENCE
Fig. 4. Coherence and relaxation of protein spin. (A) Rabi oscillation of single spin label measured by
using the sequence in Fig. 1B (fixing t0 and RF frequency at middle peak, varying t). The solid curve is a fit
using a sine function with exponential damping. (B) The red circles are measured by the double electron-electron resonance sequences on NV sensor and protein spin (fixing t equal to spin label p pulse and RF
frequency to the central peak, varying t0). The black dot is the NV center decoherence curve without protein
spin flipping. The solid curves show the best simulation of both of the experimental results in (B), corresponding to a relaxation time of 4 ms for the spin label and 90 kHz coupling between spin label and NV center.