20.109(S07): Calcium signaling in vivo

Introduction

Biological systems are complex.

Molecular insights into cell physiology have led to amazing understanding of the factors that influence a cell's behavior. Cells can be described biochemically, using binding constants and reactions rates to calculate expected concentrations of biologically relevant molecules. Cells can be described genetically, using large deletion sets and clever screens to identify critical regions of the genome and phenotypes associated with their malfunction or loss, singularly or in groups. Cells can be described structurally, with powerful microscopes capable of viewing molecules in cells "in action" and through crystalization of cellular components to give an atomic level, three-dimensional picture of cellular proteins and complexes. Often the most compelling and complete story comes from an approach that combines techniques or that asks one questions different ways.

Blind Men and an Elephant, see poemby John Godfrey Saxe

Yet for all the precision with which some aspects of the cell can be described and the volume of data being generated from high throughput analysis and genome sequencing projects, our understanding of a cell is still incomplete. Despite having a good parts list, and a good understanding of what many parts do, we are frustratingly far from "calculating life." Even simple cell like E. coli is hard to model. The great challenge is this: to describe (or better still: to build) a quantitative and predictive model for a cell's dynamic behavior. Wouldn't it be great if we could perturb a "virtual cell" and see it react as a real cell would, even if we haven't ever tried perturbing a real cell in the same way? The possibilities for discovery and engineering expand tremendously when experiments can be correctly simulated.

What is holding us back from this goal? Cell to cell variation for starters, though some efforts to model "noise" and recast it as strategy for evolution are underway, e.g. in the Elowitz lab and in the van Oudenaarden lab. Additionally, the evolution of living systems makes them hard to understand and even harder to model...nature continues to solve environmental demands in clever and novel ways. Finally, existing methods for experimentally testing and measuring the behavior of cells are limited. Measurements of single cells can be particularly noisy and difficult to correlate with bulk measurements made on populations of cells. Moreover, the measurement methods themselves are difficult to correlate with eachother, giving meaningful data in different ranges and with different sensitivities. This last point is what we will explore today, specifically asking how two techniques for measuring Ca2+ in cells compare.

Protocols

Part 1: Modeling Ca2+ signals

We know what properties we'd like sensors to have. In no particular order:

• it should be bright
• it should respond linearly and quickly to large range of inputs, [Ca2+] for today
• it should be sensitive to even subtle, single cellular stimuli
• it should be inexpensive
• it should not be disruptive to other cellular activities
• it should be easy to use

You've read that genetically encoded calcium sensors have great benefits but how well do they compare to synthetic (aka chemical) sensors like Fura-2, Fluo4-FF or X-Rhod-5F?

Consider the comparison made in the article by Pologruto, Yasuda, and Svoboda J Neurosci (2004)24:9572. These authors

1. try to correlate fluorescence with cellular activity by comparing fluorescence and chemical indicator (finding: fluorescence is nonlinear indicator at low activity levels)
2. try to correlate fluorescence with [Ca2+] (finding: complex relationship)
3. compare readout in cells with in vitro values since other CaM exist in cells and may influence sensitivity (finding: diffusion not influenced by CaM-binding proteins).

terms

• F
  • fluorescence from indicator (for GECI and for chemical indicators)
  • factors influencing F
    • Ca2+ fluctuation…so waited until reached equilibrium, defined F0 as baseline, average F for 200 msec after equilibration and before stimulation
    • photobleaching of indicator…measured as ~40% after 50 minutes
    • noise in PMT…measured “dark” noise for 50 msec with shutter closed then subtracted mean
• phi
  • degree to which fluorescence is saturated
• Rf
  • dynamic range of the indicator
  • = Fmax/Fmin
  • previously experimentally determined
• Kd
  • dissociation constant of Ca2+ from indicator
  • previously experimentally determined as concentration of Ca2+ for 1/2 phi
• n
  • Hill coeff, measure of coopertivity
  • also need to define “alpha” as scaling term and “beta” as non-specific term to solve for phi in terms of Kd, [Ca2+] and n

results

1. single stimuli (pg 9574)
   • “In response to a single action potential, the synthetic indicator produced robust, rapid onset fluorescence changes….In contrast, [two GECI] produced only very small fluorescence responses; these were detected above the noise only when averaging over many (8-16) trials.”
   • Fig 2A
2. variable patterns of stimuli (pg 9575)
   • “both [chemical indicators] respond to Ca2+ elevations sufficiently quickly to follow the stimulus patterns reliably. In contrast, GECI power spectra did not reveal a clear peak above the noise at the stimulus frequency , even under the most favorable conditions. Thus, unlike synthetic indicators, GECIs respond too slowly to follow individual action potentials within a burst.”
   • Fig 3
3. as quantitative measure of Ca2+ (pg 9575)
   • “GECIs have idiosyncratic and complex fluorescence saturation curves, making their use for quantitative [Ca2+] imaging problematic.”
   • Fig 5
4. interaction of GECI with CaM-binding proteins in cell (pg 9576)
   • “Because CaM (and hence GECI) properties are changed by interactions with CaM-binding proteins, assessing GECI mobility is important for the interpretation of GECI signals”
   • “In all cases, after bleaching, fluorescence recovered to >95% of the baseline fluorescence.”
   • “We conclude that GECIs are mostly freely diffusible”

Part 2: Oral presentation instruction

DONE!

For next time

1. Please download the midsemester evaluation form File:Macintosh HD-Users-nkuldell-Desktop-MidsemesterEval 20.109.doc. Complete the questionnaire and then print it out without including your name to turn in next time. If there is something you'd like to see done differently for the rest of the course, this is your chance to lobby for that change. Similarly, if there is something you think the class has to keep doing, let us know that too.
2. Prepare a ten-minute oral presentation of a primary research paper related in topic to the experiments performed in this experimental. Some articles that are suitable for presentation are listed under the link for the next lab. These can be reserved on a first come/first served basis so email your choice as soon as you’ve decided. Alternatively, you’re also welcome to present a research idea stemming from the experiments you have performed in Module 2.