This paper describes a protocol for cognitive assessments for genetic models of the Alzheimer's disease using the IntelliCage system, which is a high throughput automated behavioral monitoring system with operant conditioning.
Multiple factors—such as aging and genes—are frequently associated with cognitive decline. Genetically modified mouse models of cognitive decline, such as Alzheimer's disease (AD), have become a promising tool to elucidate the underlying mechanisms and promote the therapeutic advances. An important step is the validation and characterization of expected behavioral abnormality in the models, in the case of AD, cognitive decline. The long-term behavioral investigations of laboratory animals to study the effect of aging demand substantial efforts from researchers. The IntelliCage system is a high-throughput and cost-effective test battery for mice that eliminates the need for daily human handling. Here, we describe how the system is utilized in the long-term phenotyping of a genetic Alzheimer's disease model, specifically focusing on the cognitive functions. The experiment employs repeated battery of tests that assess spatial learning and executive functions. This cost-effective age-dependent phenotyping allows us to identify the transient and/or permanent effects of genes on various cognitive aspects.
The development of animal models for neuronal disease over the last decade has provided a mechanistic understanding of their basis and in order to promote the therapeutic advances1,2,3. Application of a high-throughput behavioral test battery in genetic animal models is a heuristic research tool to investigate the underlying mechanisms of human diseases and identification of drug therapies. Research test batteries adapted for long-term observation of aging and/or dementia models have traditionally forced laboratories to consume great amounts of specialized manpower and time. A home-cage monitoring system would be a cost-effective strategy as it would reduce the cost of behavioral observation by humans. Some research teams have developed automated vision-based tools that assist behavioral phenotyping of a single individual in a small home cage4,5,6. However, such methods limit the social interaction, the size of testing environments, and the variety of behavioral measures that include cognitive functions. The IntelliCage is a second-generation home-cage monitoring system designed to perform various cognitive tasks in a social home cage. Importantly, this method can eliminate daily handling that enables us to perform long-term behavioral monitoring with assessment of cognitive functions, and it can eliminate the requirements for specialized practical handling, and enable highly reproducible data acquisition7. Here, we describe the long-term phenotyping and validation in genetic mouse models of Alzheimer's disease (AD) which has been generated recently8,9,10 using the automated home-cage monitoring system. A test battery, which included assessments of spatial learning and executive functions, was repeatedly performed at multiple age points (9–12 and 14–17 months old). This age-dependent phenotyping allowed us to identify the transient and/or permanent effects of genes on various cognitive aspects. We found that some AD models showed both transient and permanent phenotypes of several cognitive aspects tested in the long-term analysis using the automated home-cage monitoring system10. Thus, the automated study using home-cage monitoring system is beneficial and cost-effective for long-term behavioral phenotyping and validation in various models of cognitive dysfunction.
All the procedures were approved by the institutional animal care and use committee, and they were carried out according to the RIKEN Brain Science Institute’s guidelines for animal experimentation.
1. Setting Apparatus
NOTE: An overview of the automated home-cage monitoring system is shown in Figure 1. Each system (39 cm x 58 cm x 21 cm) contains one microprocessor, and four corner chambers, each of which has two water bottles and a ring antenna for detecting radio-frequency identification of the transponders implanted into the animals (Figure 1A). The identification numbers of the microprocessor are defined by the rotary selector (hardware addresses) (Figure 1B). The identification numbers of the microprocessor should not overlap. Two doors in each corner are controlled by computers, which are used for the operant conditioning (Figure 1C). Typically, each cage can assess up to 12 mice (see Figure 2 as example of group housing). Using a larger number of mice is acceptable. However, one should ensure that the mice do not fight excessively and that they are not overcrowded when they perform strongly competitive tasks.
2. Software
NOTE: All three components of software ("Designer", "Controller", and "Analyzer") for the automated home-cage monitoring system have been designed as graphical user interfaces (Figure 3). Users can easily control or add various functions during the experiment.
3. Animal Preparation
4. Running Experiments
NOTE: Mice are fed ad libitum with standard mouse chow and maintained with synthetic bedding that is changed every 1 or 2 weeks depending on the task schedule. Avoid changing bedding during spatial learning task especially initial 1–2 days. Lights are on between 08:00 and 20:00. The experimental modules are sequentially performed according to the scientific questions. The experimental schedule is illustrated in Figure 5.
In our previous study, the age-dependent cognitive deficits in AD models were detected by the experiments using the automated home-cage monitoring system10. Their performance of AD models in PP was intact in both young adults and older subjects; however, the performance in PPR was significantly and progressively impaired (Figure 6). It is also important to observe the general behavior or anxiety in the adaptation phase because such traits may affect the cognition15. The AD models did not show any gross abnormalities in the visit, nosepoke, and licking numbers in the FA, NP, and DSA sessions. Thus, the AD models may have lower flexibility.
To assess the executive functions, the behavioral performance in SRT and DD was recorded. Older AD models (NL-G-F) lacked accuracy in the last stage of SRT (SRT-Test 2) (Figure 8). Facilitated compulsivity was observed in young adults and old subjects of the NL-G-F mice (Figure 9B, top). Interestingly, although there was an increase in the compulsivity of the NL-F mice who were young adults, in old age, it became comparable to that of the wild-type mice (Figure 9B, bottom). This is an example of the transient phenotype of the NL-F mutation.
Figure 1: Components of the automated home-cage monitoring system. (A) Overview of the system. (B) Location of connectors. (C) Corner chamber with operant door. Please click here to view a larger version of this figure.
Figure 2: Example of the group housing for the study. Typically, 12 mice per cage are used. In the case of using four groups (three genetic models and one wild-type), three mice per group per IntelliCage are considered adequate. Please click here to view a larger version of this figure.
Figure 3: Software for the system. (A) The "Designer" is used to build experimental files. Left: The Animal list part includes the information of the animal and group definitions. Right: The Module Space is used to define experimental operations. (B) The "Controller" can be used to run, monitor, and record experiments. Left: The status of one of the cage working with displaying animals' visit, nosepoke, and licking in the four corners. Right: Alarm windows will appear if some troubles happen. (C) The Analyzer can be used to handle and export data acquired by the Controller. Left: All data are tagged with information of the animal, environment inside the cage, and time. Filtering will help further analysis in Excel or other analytic software. Right: The time-line of the visits (also nosepoke or licks) can be shown individually. Please click here to view a larger version of this figure.
Figure 4: Transponder implantation. (A) Microchip of the transponder (DataMars). (B) Side view of the transponder implantation (transpondering). Avoid cause injury on spinal cord. (C) Top view of the transponder. Please click here to view a larger version of this figure.
Figure 5: Time-line of the experiments for the cognitive assessment with the automated home-cage monitoring system. A test battery for cognitive assessment was performed twice (1st set, 9–12 months old; and 2nd sets, 14–17 months old) followed by experiments for assessing general activity in the end (3rd set [18 months old]). This battery was designated for assessing multiple cognitive domains (indicated by colors — Red: general activity; Blue: spatial learning and memory; Green: executive function), that has advantages in validation and characterization of the expected cognitive deficits. FA: Free Adaptation; NPA: Nosepoke Adaptation; DSA: Drinking Session Adaptation; PP: Place Preference; PPR: Place Preference Reversal; SRT: Serial Reaction Time (for impulsivity and attention); PA: Place Avoidance; DD: Delay Discounting. Please click here to view a larger version of this figure.
Figure 6: Experimental design and representative results of PP and PPR tasks. (A) Top, an example module design for PP or PPR. Bottom, correct corner setting is changed to opposite side in PPR. (B) Deficits in spatial reversal learning in an AD model (NL-G-F) elicited at older age. Data are expressed as mean ± standard error of mean (SEM). ∗p <0.05; ∗∗p <0.01. Colors indicate groups of comparison: Blue: NL vs WT; Red: NL-F vs WT; Green: NL-G-F vs WT. This figure has been modified from reference10. Please click here to view a larger version of this figure.
Figure 7: Trial flow of SRT tasks. Left: the trial flow of the SRT (imp). The first nosepoke defines the correct side, and initiates a delay period (0.5–4.0 s), after which yellow LEDs are turned on. The door is then opened. Right: the trial flow of the SRT (att). The first nosepoke defines the correct side, and initiates a delay period (2.0 s), after which yellow LEDs are turned on in shorter time (0.2–1.0 s). The mice are provided with a time period during which nosepokes are allowed (the limited hold, 2 s). The doors open (5 s) only after a correct nosepoke, which is the first nosepoke during the limited hold. Please click here to view a larger version of this figure.
Figure 8: Experimental design and representative results of SRT tasks. (A) An example module design for SRT. (B) Attention decline in an AD model specific to at older age. Data are expressed as mean ± SEM. ∗p <0.05; ∗∗p <0.01. Colors indicate groups of comparison: Blue: NL vs WT; Red: NL-F vs WT; Green: NL-G-F vs WT. This figure has been modified from the reference10. Please click here to view a larger version of this figure.
Figure 9: Experimental design and representative results of DD task. (A) An example of the module for the DD task. (B) Facilitated compulsivity in the AD model (NL-G-F) at both young and old ages. This is an example of phenotype. On the other hand, compulsivity was transiently increased in another AD model (NL-F). Data are expressed as mean ± SEM. ∗p <0.05; ∗∗p <0.01. Colors indicate groups of comparison: Blue: NL vs WT; Red: NL-F vs WT; Green: NL-G-F vs WT. This figure has been modified from the previous work10. Please click here to view a larger version of this figure.
This paper describes the method using the automated home-cage monitoring system for long-term cognitive and behavioral assays in genetically modified AD models. The most critical step is the implantation of the transponder in the appropriate position. Before performing the implantation, ensure that the expiration date of the transponder has not passed. The second important point is to check the functioning of the system daily, especially as a minor problem can subsequently become a more serious one during the study (i.e., a stacked door, fallen-out transponder, bad electrical connections, etc.). Third, it is essential to be able to trouble shoot because many issues may occur throughout the experimental schedule.
This paper introduced a basic package of tasks for cognitive assessment. These tasks were produced by referring to the conventional behavioral tests, but they cannot perfectly mimic the conventional tests. For example, SRT tasks do not set choice-mode. Five choice serial choice task (5CSRTT), a model of SRT tasks, is normally conducted in a chamber with 9, 5 or 3 holes for the nosepoke to measure the accuracy16,17. Our team previously tried to implement a choice version of SRT, but the mice failed to learn the rule within relatively long time (2 weeks or more). The user of this system should be aware of the difference, and discuss data carefully.
Another issue is about the limitations for the repeated measurement. As mentioned in previous study18, the first one is that the repeated experiments cannot simply compare the effect of age. We observed that the data from the second PA test failed to replicate the memory impairment in AD models10. The performance of all genotype was much worse than that of the first PA test. This difference may come from age difference or habituation to the aversive stimulus (experience of previous PA task). The experimenters should mind the repeat effect and consider the order of the tasks. To overcome the habituation of aversive stimulus in the second PA test, it might be better to use stronger air-puff stimulus or to add some novel environmental stimulus such as different types of bedding or sound19.
Various researchers have been developing new methods and protocols for the automated home-cage monitoring studies20,21,22,23,24,25,26, and supporting software using open-source library27. Hence, the possibility of the system has been expanded. Finally, the system provides automated high-throughput behavioral screening for long-term assays that are used to study a wide-range of cognitive functions, which is beneficial in phenotyping and validating disease models.
The authors have nothing to disclose.
We thank Reiko Ando for her help in photographing materials. This research was supported by Grant-in-Aid for Exploratory Research (JSPS KAKENHI Grant Number 16K15196).
IntelliCage | TSE Systems | – | Parchased in 2011 or later |
PC | Dell | Inspiron 580s | – |
Display | Dell | SI75T-WL | – |
ALPHA-dri | Shepherd Specialty Papers | – | Standard bedding |
Aron Alpha (Krasy Glue) 2g | Toagosei (Krasy Glue) | #04612 | Cyanoacrylates for gluing magnet and blak arm |
Handheld Transponder Reader | BTS-ID | R-560 | Transponder reader, which reads both Trovan and DataMars |
Transponder | DataMars | T-VA, T-VAS, or another series | Basic package of transponders and implanters |
Diamond Grip Plus | Ansel Microflex | DGP-INT-M | Experimental glove |
Isoflurane | Pfizer | 1119701G1092 | – |
Vaporizer for small animals | DS Pharma Biomedical | SF-B01 | Facemask included |
Neo-Medrol | Pfizer | 006472-001 | Eye ointment |
Ethanol (70%) | – | – | – |
Excel | Microsoft | 00202-51382-15524-AA928 | For data analysis |