A high-throughput method to deliver targeted optogenetic stimulation to moving C. elegans populations

Abstract

We present a high-throughput optogenetic illumination system capable of simultaneous closed-loop light delivery to specified targets in populations of moving Caenorhabditis elegans. The instrument addresses three technical challenges: It delivers targeted illumination to specified regions of the animal's body such as its head or tail; it automatically delivers stimuli triggered upon the animal's behavior; and it achieves high throughput by targeting many animals simultaneously. The instrument was used to optogenetically probe the animal's behavioral response to competing mechanosensory stimuli in the the anterior and posterior gentle touch receptor neurons. Responses to more than 43,418 stimulus events from a range of anterior-posterior intensity combinations were measured. The animal's probability of sprinting forward in response to a mechanosensory stimulus depended on both the anterior and posterior stimulation intensity, while the probability of reversing depended primarily on the anterior stimulation intensity. We also probed the animal's response to mechanosensory stimulation during the onset of turning, a relatively rare behavioral event, by delivering stimuli automatically when the animal began to turn. Using this closed-loop approach, over 9,700 stimulus events were delivered during turning onset at a rate of 9.2 events per worm hour, a greater than 25-fold increase in throughput compared to previous investigations. These measurements validate with greater statistical power previous findings that turning acts to gate mechanosensory evoked reversals. Compared to previous approaches, the current system offers targeted optogenetic stimulation to specific body regions or behaviors with many fold increases in throughput to better constrain quantitative models of sensorimotor processing.

Fig 1. Closed-loop targeted optogenetic delivery system.

(a) Schematic of system. A projector simultaneously delivers patterned targeted optogenetic stimulation to multiple worms on an agar plate in real time. (b) Photograph of instrument. (c) Image from experiment shows animals expressing Chrimson in touch receptor neurons (AML470) as they crawl on agar. Corresponding video is shown in S1 Video. Tracked paths are shown in yellow. Green and white dots in the center relate to a visual time stamping system and are excluded from analysis. Inset shows details of an animal receiving optogenetic stimulation targeted to its head and tail (0.5-mm diameter stimuli). The two white circle in the inset show the targeted location of the stimulus. Red shading shows area where stimulus was delivered.

Fig 2. Stimulation of anterior mechanosensory neurons evokes reverse behavior; stimulation of posterior mechanosensory neurons evokes sprints.

Targeted stimulation is delivered to worms expressing Chrimson in their gentle touch mechanosensory neurons (strain AML470). (a–d) Representative velocity trace from single stimulus event. Dashed lines indicate timing of stimulation (1 second, 0.5-mm diameter circular illumination pattern centered at the tip of the animal’s head and/or tail, red light intensity of 80 uW/mm²). (e–h) A total of 20 randomly sampled velocity traces for each stimulus condition are shown, sorted by mean velocity immediately after stimulation. Same as in S2–S5 Videos. Arrow indicates each representative trace shown in a–d. (i–l) Probability density of velocities for each stimulus condition. Mean velocity is shown as black line. n>1,500 stimulus events per condition. (m–p) The fraction of animals occupying each of 4 velocity-defined behavioral states is shown for the time point 2 seconds before stimulation onset and immediately after stimulation. Cutoffs for behavior states are shown in S3 Fig. p-Values calculated using Wilcoxon rank sum test. Error bars represents 95% confidence intervals estimated using 1,000 bootstraps. Numerical values are listed in S1 Data.

Fig 3. Behavioral response to competing stimulation of anterior and posterior mechanosensory neurons.

Various combinations of light intensity was delivered to the head and tail of worms expressing Chrimson in gentle touch mechanosensory neurons (strain AML470, n = 43,418 stimulus events total, supserset of data shown in Fig 2). (a) Probability of transitioning into reverse, (b) pause/slow, (c) forward, and (d) sprint behaviors are shown individually (e) and all together as pie charts. (f) The gradient of the plane of best fit is shown as a vector for each behavior. Fitting was performed using methods of least squares. Error-bars are 95% confidence intervals. Numerical values are listed in S2 Data.

Fig 4. Probability of reversing in response to a mechanosensory stimulus is higher for stimuli that arrive during forward locomotion than for stimuli that arrive during turning.

Response to whole body optogenetic illumination of gentle touch mechanosensory neurons is shown for stimuli that arrive during either forward or turning behaviors for 2 strains of nominally identical genotypes, (a) AML67 and (b) AML470. Stimuli delivered during turns are from closed-loop optogenetic experiments, while stimuli delivered during forward locomotion are from open-loop experiments. Three seconds of 80 uW/mm² illumination was delivered in the experiment condition. Only 0.5 uW/mm² was delivered for control condition. Error bars are 95% confidence interval calculated via 10,000 bootstraps. Z-test was used to calculate significance. *** indicates p<0.001. p-Value for AML67 control group is 0.549. p-Value for AML470 control group is 0.026. The number of stimulus events for each condition (from left-most bar to right-most bar) are 5,968, 1,551, 5,971, and 1,448 for AML67 and 2,501, 1,543, 2,676, and 1,438 for AML470. Machine-readable numerical values are listed in S3 and S4 Data.

Fig 5. Selected software modules involved in closed-loop light stimulation.

Selected software modules are shown that run synchronously or asynchronously. Note that the image processing depiction is illustrative and for a small cropped portion of the actual field of view.

Fig 6. GUI shown here during an experiment.

GUI, graphical user interface.

Fig 7. Illustration of the fast centerline finding algorithm.

The algorithm proceeds in order from left to right. A binary image of the worm is taken as input. A skeleton is then generated through morphological thinning. The first recursive algorithm starts from an endpoint of the skeleton and breaks it down into segments at each branch point. The second recursive algorithm uses these segments to find the longest contiguous segment from one end point to another end point.