Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer-reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state-of-the-art handbook for basic and clinical researchers

A signature of attractor dynamics in the CA3 region of the hippocampus

The notion of attractor networks is the leading hypothesis for how associative memories are stored and recalled. A defining/nanatomical feature of such networks is excitatory recurrent connections. These ‘‘attract’’ the firing pattern of the network to/na stored pattern, even when the external input is incomplete (pattern completion). The CA3 region of the hippocampus has/nbeen postulated to be such an attractor network; however, the experimental evidence has been ambiguous, leading to the/nsuggestion that CA3 is not an attractor network. In order to resolve this controversy and to better understand how CA3/nfunctions, we simulated CA3 and its input structures. In our simulation, we could reproduce critical experimental results and/nestablish the criteria for identifying attractor properties. Notably, under conditions in which there is continuous input, the/noutput should be ‘‘attracted’’ to a stored pattern. However, contrary to previous expectations, as a pattern is gradually/n‘‘morphed’’ from one stored pattern to another, a sharp transition between output patterns is not expected. The observed/nfiring patterns of CA3 meet these criteria and can be quantitatively accounted for by our model. Notably, as morphing/nproceeds, the activity pattern in the dentate gyrus changes; in contrast, the activity pattern in the downstream CA3 network/nis attracted to a stored pattern and thus undergoes little change. We furthermore show that other aspects of the observed/nfiring patterns can be explained by learning that occurs during behavioral testing. The CA3 thus displays both the learning/nand recall signatures of an attractor network. These observations, taken together with existing anatomical and behavioral/nevidence, make the strong case that CA3 constructs associative memories based on attractor dynamics.This study was supported by National Institutes of Health Awards R01DA027807 and R01MH102841, by the European Commission through the projects Goal-Leaders (FP7-ICT- 270108) and Experimental Functional Android Assistant (FP7-ICT- 270490), and by the Brazilian agency CAPES through the Science Without Borders program (CSF-BJT 040/2012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript