Coordinated transitions between mutually exclusive motor states are central to behavioral decisions. During locomotion, the nematode Caenorhabditis elegans spontaneously cycles between forward runs, reversals, and turns with complex but predictable dynamics. Here we provide insight into these dynamics by demonstrating how RIM interneurons, which are active during reversals, act in two modes to stabilize both forward runs and reversals. By systematically quantifying the roles of RIM outputs during spontaneous behavior, we show that RIM lengthens reversals when depolarized through glutamate and tyramine neurotransmitters and lengthens forward runs when hyperpolarized through its gap junctions. RIM is not merely silent upon hyperpolarization: RIM gap junctions actively reinforce a hyperpolarized state of the reversal circuit. Additionally, the combined outputs of chemical synapses and gap junctions from RIM regulate forward-to-reversal transitions. Our results indicate that multiple classes of RIM synapses create behavioral inertia during spontaneous locomotion.

Behavioral states were extracted from the State array generated by BargmannWormTracker. The tracker can be accessed at: https://github.com/navinpokala/BargmannWormTracker

Local search event frequencies per minute were calculated 4-8 min after removal from food. Global search frequencies per minute were calculated 36-40 min after removal from food. Only tracks that were continuous for the entire four-minute time interval were included in frequency analysis. When calculating frequencies, tracks taken on a single day from a single assay plate were averaged to give a single data point, e.g. in Figure 2B and 2D.

Distributions of reversal parameters and forward run durations were calculated using events observed during local search, 4-8 minutes after removal from food. All reversals were included; only forward runs over 2 s in length were included. Reversal length is the path length calculated using the X-Y coordinates, worm length, and pixel size extracted from the tracker. Reversal and forward run speed are the average of mean and median speed extracted from the tracker.

Tracks that were less than 5 minutes long, tracks approaching the copper barrier, and tracks that did not include a complete reversal or forward run were not included in reversal and forward run parameter analyses.

Custom MATLAB functions used to generate these data sets are included here (SordilloCode1.m, SordilloCode2.m).

Imaging data was tracked using an ImageJ Macro originally used in in Larsch et al., 2013 (https://doi.org/10.1073/pnas.1318325110). Tracks were cleaned and processed using custom Python and  MATLAB functions included here (SordilloCode3.m, SordilloCode4). 

SordilloBargmann2021

This repository contains data files pertaining to Sordillo, A. and Bargmann, C. I. (2021).

Questions about this repository can be directed towards Aylesse Sordillo at aylesse.sordillo@gmail.com or Cori Bargmann at cori@rockefeller.edu

These data were collected in the Bargmann Lab at Rockefeller University in New York City from 2017-2021.

Funding was provided by HHMI and Chan Zuckerberg Science Initiative.

1) Data sets relevant to all of distributions in the main figures (excluding the optogenetics experiments) are included as both a .mat file that contains a MATLAB structure and a .csv file. Data sets from RIC supplements are also included for those interested.

Each genotype (or transgenic line) tested has its own files.  Files are named by experiment and then genotype/line.

All data provided are from 4-8 min off food during local search.

Corresponding rows for reversal and forward run parameters are from the same event.

Analysis was completed using custom MATLAB functions included here (SordilloCode1.m, SordilloCode2.m).

Nested MATLAB structures were converted to .csv files using the following:

Gero Nootz (2021). Nested Structure to table and or text file (https://www.mathworks.com/matlabcentral/fileexchange/51271-nested-structure-to-table-and-or-text-file). MATLAB Central File Exchange. Retrieved January 17, 2020.

Description of Variables:

Genotype: A label describing the genotype or strain in the dataset

Experiment: A label describing the experiment

AllReversals: includes all reversals (Reversal Omegas, Pure Reversals, other)

ReversalOmegas: reversals coupled to sharp omega turns

PureReversals: reversals followed by immediate forward movement

ForwardRuns: forward crawls (as opposed to reversals) Forward Runs with < 2 s

duration are not included.

Length: reversal length (body lengths)

Speed: reversal or forward run speed (mm/s).

Duration: reversal or forward run duration (s).

Events per animal per minute: each value represents the count of a behavioral event per animal divided by the number of minutes (4).

                        AllReversals: includes all reversals (Reversal Omegas, Pure Reversals, other)

ReversalOmegas: reversals coupled to sharp omega turns

PureReversals: reversals followed by immediate forward movement

LongReversals: reversals > 0.5 body lengths

ShortReversals: reversals < 0.5 body lengths

ForwardRuns: forward crawls (as opposed to reversals, not filtered by duration).

Pauses: animal is not moving forward or backward (based on low velocity, see Bargmann Lab Tracker).

2) A source data file (Sordillo_SourceData1_0928.xlxs) includes data pertaining to all dot plots labeled by figure panel. This file includes source data pertaining to all dot plots labeled by figure panel.  The numerical value represents the average frequency of a behavioral event, per animal, per minute, on a single assay plate.

3) A source data file (Sordillo_SourceData2_0928.xlxs) includes source data for Figure 5- figure supplement 3: each entry describes RIM calcium imaging data from a single 5 minute-long trace, either at 0-5 minutes or 30-35 minutes after removal of conditioned medium. Figure5-figure supplement 3C: fraction time RIM was ON in each trace. Figure5-figure supplement 3D: count of RIM transitions to the ON state in each trace. Figure5-figure supplement 3E: mean change in GCaMP fluorescence between the OFF and ON states for each trace. 

4) Imaging data (Figure 5-figure supplement3) was tracked using an ImageJ Macro originally used in in Larsch et al., 2013 (https://doi.org/10.1073/pnas.1318325110). Tracks were cleaned and processed using custom MATLAB and Python functions included here (SordilloCode3.m, SordilloCode4).

Also see ReadMe.txt.